RSK World - Action Recognition Dataset - Project Files Browser | RSK World

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<!--
==================================================================================
    Action Recognition Dataset - Video Action Recognition for AI/ML Training
==================================================================================
    Project: Action Recognition Dataset
    Category: Video Data / Data Science
    
    Description: Video action recognition dataset with labeled video sequences 
    for training action classification and video understanding models.
    
==================================================================================
    DEVELOPER INFORMATION
==================================================================================
    Website: RSK World (https://rskworld.in)
    Founded by: Molla Samser
    Designer & Tester: Rima Khatun
    
    Contact Information:
    - Email: help@rskworld.in
    - Email: support@rskworld.in
    - Phone: +91 93305 39277
    
    Social Media: Follow us on our social platforms for updates
    
==================================================================================
    COPYRIGHT & LICENSE
==================================================================================
    © 2026 RSK World. All Rights Reserved.
    This dataset is provided for educational and research purposes.
    
    For commercial use or content removal requests, please contact:
    support@rskworld.in
    
==================================================================================
    TECHNOLOGIES USED
==================================================================================
    - MP4 & AVI Video Formats
    - OpenCV for Video Processing
    - HTML5, CSS3, JavaScript
    - Font Awesome Icons
    - Google Fonts
    
==================================================================================
-->
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                <span>Advanced Dataset</span>
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            <h1 class="hero-title">
                <span class="title-line">Action Recognition</span>
                <span class="title-line gradient-text">Video Dataset</span>
            </h1>
            <p class="hero-description">
                Comprehensive video action recognition dataset with labeled sequences for training 
                3D CNNs, video classification models, and video understanding applications.
            </p>
            <div class="hero-stats">
                <div class="stat">
                    <i class="fas fa-video"></i>
                    <div class="stat-info">
                        <span class="stat-number" data-count="1500">0</span>
                        <span class="stat-label">Video Clips</span>
                    </div>
                </div>
                <div class="stat">
                    <i class="fas fa-tags"></i>
                    <div class="stat-info">
                        <span class="stat-number" data-count="15">0</span>
                        <span class="stat-label">Action Classes</span>
                    </div>
                </div>
                <div class="stat">
                    <i class="fas fa-clock"></i>
                    <div class="stat-info">
                        <span class="stat-number" data-count="50">0</span>
                        <span class="stat-label">Hours of Video</span>
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                <a href="action-recognition.zip" class="btn btn-primary" download>
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                    <span class="tech-tag"><i class="fas fa-film"></i> MP4</span>
                    <span class="tech-tag"><i class="fas fa-video"></i> AVI</span>
                    <span class="tech-tag"><i class="fas fa-eye"></i> OpenCV</span>
                    <span class="tech-tag"><i class="fas fa-cog"></i> Video Processing</span>
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        <div class="hero-visual">
            <div class="video-showcase">
                <div class="video-frame">
                    <div class="frame-header">
                        <div class="frame-dots">
                            <span class="dot red"></span>
                            <span class="dot yellow"></span>
                            <span class="dot green"></span>
                        </div>
                        <span class="frame-title" id="videoTitle">dancing_001.mp4</span>
                    </div>
                    <div class="frame-content">
                        <div class="video-preview">
                            <video id="heroVideo" autoplay loop muted playsinline class="hero-real-video">
                                <source src="sample_data/train/dancing/dancing_001.mp4" type="video/mp4">
                            </video>
                            <div class="action-label">
                                <span class="label-text" id="actionLabel">Dancing</span>
                                <span class="confidence">Confidence: <span id="confidenceValue">98.5%</span></span>
                            </div>
                        </div>
                        <div class="video-controls">
                            <button class="video-nav-btn" id="prevVideo" title="Previous Video">
                                <i class="fas fa-chevron-left"></i>
                            </button>
                            <div class="video-indicator">
                                <span id="currentVideoIndex">1</span> / <span id="totalVideos">8</span>
                            </div>
                            <button class="video-nav-btn" id="nextVideo" title="Next Video">
                                <i class="fas fa-chevron-right"></i>
                            </button>
                        </div>
                        <div class="video-timeline">
                            <div class="timeline-progress" id="timelineProgress"></div>
                        </div>
                    </div>
                </div>
                <!-- Video Thumbnails -->
                <div class="video-thumbnails">
                    <div class="thumbnail active" data-video="sample_data/train/dancing/dancing_001.mp4" data-label="Dancing">
                        <i class="fas fa-music"></i>
                        <span>Dancing</span>
                    </div>
                    <div class="thumbnail" data-video="sample_data/train/running/running_001.mp4" data-label="Running">
                        <i class="fas fa-running"></i>
                        <span>Running</span>
                    </div>
                    <div class="thumbnail" data-video="sample_data/train/walking/walking_001.mp4" data-label="Walking">
                        <i class="fas fa-walking"></i>
                        <span>Walking</span>
                    </div>
                    <div class="thumbnail" data-video="sample_data/train/jumping/jumping_001.mp4" data-label="Jumping">
                        <i class="fas fa-arrow-up"></i>
                        <span>Jumping</span>
                    </div>
                    <div class="thumbnail" data-video="sample_data/train/yoga/yoga_001.mp4" data-label="Yoga">
                        <i class="fas fa-spa"></i>
                        <span>Yoga</span>
                    </div>
                    <div class="thumbnail" data-video="sample_data/train/stretching/stretching_001.mp4" data-label="Stretching">
                        <i class="fas fa-child"></i>
                        <span>Stretching</span>
                    </div>
                    <div class="thumbnail" data-video="sample_data/train/exercising/exercising_001.mp4" data-label="Exercising">
                        <i class="fas fa-dumbbell"></i>
                        <span>Exercise</span>
                    </div>
                </div>
            </div>
        </div>
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    <!-- Overview Section -->
    <section class="section overview" id="overview">
        <div class="container">
            <div class="section-header">
                <span class="section-badge">About Dataset</span>
                <h2 class="section-title">What's Inside?</h2>
                <p class="section-description">
                    A comprehensive collection of video sequences with action labels, 
                    perfect for training state-of-the-art video understanding models.
                </p>
            </div>
            <div class="overview-grid">
                <div class="overview-card main-card">
                    <div class="card-icon">
                        <i class="fas fa-database"></i>
                    </div>
                    <h3>Dataset Overview</h3>
                    <p>
                        This dataset contains carefully curated video sequences with precise action labels 
                        for action recognition tasks. Perfect for training 3D CNNs, video classification 
                        models, and video understanding applications.
                    </p>
                    <ul class="feature-list">
                        <li><i class="fas fa-check"></i> High-quality video sequences</li>
                        <li><i class="fas fa-check"></i> Precise frame-level annotations</li>
                        <li><i class="fas fa-check"></i> Multiple action categories</li>
                        <li><i class="fas fa-check"></i> Ready for deep learning models</li>
                    </ul>
                </div>
                <div class="overview-card">
                    <div class="card-icon purple">
                        <i class="fas fa-brain"></i>
                    </div>
                    <h3>Use Cases</h3>
                    <ul class="use-case-list">
                        <li>Video Classification</li>
                        <li>Action Detection</li>
                        <li>Sports Analytics</li>
                        <li>Surveillance Systems</li>
                        <li>Human-Computer Interaction</li>
                    </ul>
                </div>
                <div class="overview-card">
                    <div class="card-icon cyan">
                        <i class="fas fa-code"></i>
                    </div>
                    <h3>Compatible Models</h3>
                    <ul class="use-case-list">
                        <li>3D CNN (C3D, I3D)</li>
                        <li>Two-Stream Networks</li>
                        <li>SlowFast Networks</li>
                        <li>Video Transformers</li>
                        <li>LSTM + CNN Hybrids</li>
                    </ul>
                </div>
            </div>
        </div>
    </section>

    <!-- Features Section -->
    <section class="section features" id="features">
        <div class="container">
            <div class="section-header">
                <span class="section-badge">Key Features</span>
                <h2 class="section-title">Dataset Features</h2>
                <p class="section-description">
                    Everything you need for building robust action recognition systems
                </p>
            </div>
            <div class="features-grid">
                <div class="feature-card" data-aos="fade-up">
                    <div class="feature-icon">
                        <i class="fas fa-film"></i>
                    </div>
                    <h3>Labeled Video Sequences</h3>
                    <p>High-quality video clips with accurate action labels for supervised learning</p>
                </div>
                <div class="feature-card" data-aos="fade-up" data-aos-delay="100">
                    <div class="feature-icon">
                        <i class="fas fa-layer-group"></i>
                    </div>
                    <h3>Multiple Action Classes</h3>
                    <p>Diverse action categories including walking, running, jumping, and more</p>
                </div>
                <div class="feature-card" data-aos="fade-up" data-aos-delay="200">
                    <div class="feature-icon">
                        <i class="fas fa-random"></i>
                    </div>
                    <h3>Training & Validation Sets</h3>
                    <p>Pre-split datasets for proper model training and evaluation</p>
                </div>
                <div class="feature-card" data-aos="fade-up" data-aos-delay="300">
                    <div class="feature-icon">
                        <i class="fas fa-crosshairs"></i>
                    </div>
                    <h3>Frame-Level Annotations</h3>
                    <p>Precise temporal annotations for accurate action localization</p>
                </div>
                <div class="feature-card" data-aos="fade-up" data-aos-delay="400">
                    <div class="feature-icon">
                        <i class="fas fa-cube"></i>
                    </div>
                    <h3>Ready for 3D CNN Models</h3>
                    <p>Optimized format for spatiotemporal deep learning architectures</p>
                </div>
                <div class="feature-card" data-aos="fade-up" data-aos-delay="500">
                    <div class="feature-icon">
                        <i class="fas fa-file-code"></i>
                    </div>
                    <h3>Metadata & Documentation</h3>
                    <p>Complete documentation with usage examples and best practices</p>
                </div>
            </div>
        </div>
    </section>

    <!-- Dataset Structure Section -->
    <section class="section structure" id="structure">
        <div class="container">
            <div class="section-header">
                <span class="section-badge">Organization</span>
                <h2 class="section-title">Dataset Structure</h2>
                <p class="section-description">
                    Well-organized directory structure for easy navigation and integration
                </p>
            </div>
            <div class="structure-container">
                <div class="file-tree">
                    <div class="tree-header">
                        <i class="fas fa-folder-open"></i>
                        <span>action-recognition/</span>
                    </div>
                    <ul class="tree-list">
                        <li class="tree-item">
                            <div class="item-header folder" onclick="toggleFolder(this)">
                                <i class="fas fa-folder"></i>
                                <span>train/</span>
                                <span class="item-count">12 classes</span>
                            </div>
                            <ul class="sub-tree">
                                <li class="tree-item">
                                    <div class="item-header folder" onclick="toggleFolder(this)">
                                        <i class="fas fa-folder"></i>
                                        <span>walking/</span>
                                        <span class="item-count">150 videos</span>
                                    </div>
                                </li>
                                <li class="tree-item">
                                    <div class="item-header folder" onclick="toggleFolder(this)">
                                        <i class="fas fa-folder"></i>
                                        <span>running/</span>
                                        <span class="item-count">120 videos</span>
                                    </div>
                                </li>
                                <li class="tree-item">
                                    <div class="item-header folder" onclick="toggleFolder(this)">
                                        <i class="fas fa-folder"></i>
                                        <span>jumping/</span>
                                        <span class="item-count">100 videos</span>
                                    </div>
                                </li>
                                <li class="tree-item">
                                    <div class="item-header folder" onclick="toggleFolder(this)">
                                        <i class="fas fa-folder"></i>
                                        <span>sitting/</span>
                                        <span class="item-count">90 videos</span>
                                    </div>
                                </li>
                                <li class="tree-item more-items">
                                    <span>... 8 more classes</span>
                                </li>
                            </ul>
                        </li>
                        <li class="tree-item">
                            <div class="item-header folder" onclick="toggleFolder(this)">
                                <i class="fas fa-folder"></i>
                                <span>val/</span>
                                <span class="item-count">12 classes</span>
                            </div>
                            <ul class="sub-tree">
                                <li class="tree-item more-items">
                                    <span>Same structure as train/</span>
                                </li>
                            </ul>
                        </li>
                        <li class="tree-item">
                            <div class="item-header folder" onclick="toggleFolder(this)">
                                <i class="fas fa-folder"></i>
                                <span>test/</span>
                                <span class="item-count">12 classes</span>
                            </div>
                            <ul class="sub-tree">
                                <li class="tree-item more-items">
                                    <span>Same structure as train/</span>
                                </li>
                            </ul>
                        </li>
                        <li class="tree-item">
                            <div class="item-header folder" onclick="toggleFolder(this)">
                                <i class="fas fa-folder"></i>
                                <span>annotations/</span>
                                <span class="item-count">4 files</span>
                            </div>
                            <ul class="sub-tree">
                                <li class="tree-item">
                                    <div class="item-header file">
                                        <i class="fas fa-file-alt json"></i>
                                        <span>train_annotations.json</span>
                                    </div>
                                </li>
                                <li class="tree-item">
                                    <div class="item-header file">
                                        <i class="fas fa-file-alt json"></i>
                                        <span>val_annotations.json</span>
                                    </div>
                                </li>
                                <li class="tree-item">
                                    <div class="item-header file">
                                        <i class="fas fa-file-alt json"></i>
                                        <span>test_annotations.json</span>
                                    </div>
                                </li>
                                <li class="tree-item">
                                    <div class="item-header file">
                                        <i class="fas fa-file-alt json"></i>
                                        <span>class_labels.json</span>
                                    </div>
                                </li>
                            </ul>
                        </li>
                        <li class="tree-item">
                            <div class="item-header file">
                                <i class="fas fa-file-alt md"></i>
                                <span>README.md</span>
                            </div>
                        </li>
                        <li class="tree-item">
                            <div class="item-header file">
                                <i class="fas fa-file-alt txt"></i>
                                <span>LICENSE.txt</span>
                            </div>
                        </li>
                    </ul>
                </div>
                <div class="structure-info">
                    <h3>Action Classes</h3>
                    <div class="class-grid">
                        <div class="class-tag"><i class="fas fa-walking"></i> Walking</div>
                        <div class="class-tag"><i class="fas fa-running"></i> Running</div>
                        <div class="class-tag"><i class="fas fa-arrow-up"></i> Jumping</div>
                        <div class="class-tag"><i class="fas fa-chair"></i> Sitting</div>
                        <div class="class-tag"><i class="fas fa-male"></i> Standing</div>
                        <div class="class-tag"><i class="fas fa-hand-paper"></i> Waving</div>
                        <div class="class-tag"><i class="fas fa-handshake"></i> Clapping</div>
                        <div class="class-tag"><i class="fas fa-fist-raised"></i> Punching</div>
                        <div class="class-tag"><i class="fas fa-shoe-prints"></i> Kicking</div>
                        <div class="class-tag"><i class="fas fa-sync-alt"></i> Turning</div>
                        <div class="class-tag"><i class="fas fa-arrows-alt-v"></i> Bending</div>
                        <div class="class-tag"><i class="fas fa-hand-point-right"></i> Pointing</div>
                    </div>
                    <div class="format-info">
                        <h4>Video Specifications</h4>
                        <ul>
                            <li><strong>Format:</strong> MP4 / AVI</li>
                            <li><strong>Resolution:</strong> 320x240 to 1920x1080</li>
                            <li><strong>Frame Rate:</strong> 24-30 FPS</li>
                            <li><strong>Duration:</strong> 2-10 seconds per clip</li>
                            <li><strong>Color:</strong> RGB</li>
                        </ul>
                    </div>
                </div>
            </div>
        </div>
    </section>

    <!-- Usage Section -->
    <section class="section usage" id="usage">
        <div class="container">
            <div class="section-header">
                <span class="section-badge">Getting Started</span>
                <h2 class="section-title">How to Use</h2>
                <p class="section-description">
                    Quick start guide for loading and using the dataset
                </p>
            </div>
            <div class="code-examples">
                <div class="code-tabs">
                    <button class="code-tab active" data-tab="python">
                        <i class="fab fa-python"></i> Python
                    </button>
                    <button class="code-tab" data-tab="pytorch">
                        <i class="fas fa-fire"></i> PyTorch
                    </button>
                    <button class="code-tab" data-tab="tensorflow">
                        <i class="fas fa-brain"></i> TensorFlow
                    </button>
                </div>
                <div class="code-content">
                    <div class="code-panel active" id="python">
                        <pre><code class="language-python"><span class="comment"># Action Recognition Dataset Loader</span>
<span class="comment"># Created by RSK World (rskworld.in)</span>
<span class="comment"># Developer: Molla Samser</span>

<span class="keyword">import</span> cv2
<span class="keyword">import</span> os
<span class="keyword">import</span> json
<span class="keyword">import</span> numpy <span class="keyword">as</span> np

<span class="keyword">def</span> <span class="function">load_video</span>(video_path, num_frames=<span class="number">16</span>):
    <span class="string">"""Load and preprocess video frames"""</span>
    cap = cv2.VideoCapture(video_path)
    frames = []
    
    <span class="keyword">while</span> <span class="keyword">True</span>:
        ret, frame = cap.read()
        <span class="keyword">if not</span> ret:
            <span class="keyword">break</span>
        frame = cv2.resize(frame, (<span class="number">224</span>, <span class="number">224</span>))
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        frames.append(frame)
    
    cap.release()
    
    <span class="comment"># Sample frames uniformly</span>
    indices = np.linspace(<span class="number">0</span>, len(frames)-<span class="number">1</span>, num_frames).astype(int)
    <span class="keyword">return</span> np.array([frames[i] <span class="keyword">for</span> i <span class="keyword">in</span> indices])

<span class="comment"># Load class labels</span>
<span class="keyword">with</span> <span class="function">open</span>(<span class="string">'annotations/class_labels.json'</span>) <span class="keyword">as</span> f:
    class_labels = json.load(f)

<span class="keyword">print</span>(<span class="string">f"Loaded {len(class_labels)} action classes"</span>)</code></pre>
                    </div>
                    <div class="code-panel" id="pytorch">
                        <pre><code class="language-python"><span class="comment"># PyTorch Dataset for Action Recognition</span>
<span class="comment"># Created by RSK World (rskworld.in)</span>
<span class="comment"># Developer: Molla Samser</span>

<span class="keyword">import</span> torch
<span class="keyword">from</span> torch.utils.data <span class="keyword">import</span> Dataset, DataLoader
<span class="keyword">import</span> cv2
<span class="keyword">import</span> os

<span class="keyword">class</span> <span class="class-name">ActionDataset</span>(Dataset):
    <span class="keyword">def</span> <span class="function">__init__</span>(self, root_dir, split=<span class="string">'train'</span>, transform=<span class="keyword">None</span>):
        self.root_dir = os.path.join(root_dir, split)
        self.transform = transform
        self.classes = sorted(os.listdir(self.root_dir))
        self.samples = []
        
        <span class="keyword">for</span> cls_idx, cls_name <span class="keyword">in</span> <span class="function">enumerate</span>(self.classes):
            cls_path = os.path.join(self.root_dir, cls_name)
            <span class="keyword">for</span> video <span class="keyword">in</span> os.listdir(cls_path):
                self.samples.append((
                    os.path.join(cls_path, video),
                    cls_idx
                ))
    
    <span class="keyword">def</span> <span class="function">__len__</span>(self):
        <span class="keyword">return</span> <span class="function">len</span>(self.samples)
    
    <span class="keyword">def</span> <span class="function">__getitem__</span>(self, idx):
        video_path, label = self.samples[idx]
        frames = self._load_video(video_path)
        <span class="keyword">if</span> self.transform:
            frames = self.transform(frames)
        <span class="keyword">return</span> torch.FloatTensor(frames), label

<span class="comment"># Create DataLoader</span>
dataset = ActionDataset(<span class="string">'./action-recognition'</span>, split=<span class="string">'train'</span>)
loader = DataLoader(dataset, batch_size=<span class="number">8</span>, shuffle=<span class="keyword">True</span>)</code></pre>
                    </div>
                    <div class="code-panel" id="tensorflow">
                        <pre><code class="language-python"><span class="comment"># TensorFlow Dataset for Action Recognition</span>
<span class="comment"># Created by RSK World (rskworld.in)</span>
<span class="comment"># Developer: Molla Samser</span>

<span class="keyword">import</span> tensorflow <span class="keyword">as</span> tf
<span class="keyword">import</span> pathlib

<span class="keyword">def</span> <span class="function">create_dataset</span>(data_dir, batch_size=<span class="number">8</span>):
    <span class="string">"""Create TensorFlow dataset from video directory"""</span>
    data_dir = pathlib.Path(data_dir)
    
    <span class="comment"># Get class names from directory structure</span>
    class_names = sorted([item.name <span class="keyword">for</span> item <span class="keyword">in</span> data_dir.iterdir() 
                         <span class="keyword">if</span> item.is_dir()])
    
    <span class="keyword">def</span> <span class="function">process_video</span>(video_path):
        <span class="comment"># Read and decode video frames</span>
        video = tf.io.read_file(video_path)
        <span class="comment"># Process frames...</span>
        <span class="keyword">return</span> frames
    
    <span class="comment"># Create dataset</span>
    list_ds = tf.data.Dataset.list_files(<span class="function">str</span>(data_dir/<span class="string">'*/*'</span>))
    dataset = list_ds.map(process_video, 
                         num_parallel_calls=tf.data.AUTOTUNE)
    dataset = dataset.batch(batch_size).prefetch(tf.data.AUTOTUNE)
    
    <span class="keyword">return</span> dataset, class_names

<span class="comment"># Load training data</span>
train_ds, classes = create_dataset(<span class="string">'./action-recognition/train'</span>)
<span class="keyword">print</span>(<span class="string">f"Classes: {classes}"</span>)</code></pre>
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