UCSD Pedestrian Database

Data Format and Structure

The videos are record at a resolution of 238×158 pixels, with a frame rate of 10 frames per second (fps), offering a moderate-quality view of pedestrian movement while keeping file sizes manageable. All videos are in 8-bit grayscale format, minimizing color distractions and focusing on movement patterns.

The dataset is organize into directories based on different scene locations. Each directory (e.g., vidX, where X is a specific scene identifier such as “vidf”) contains multiple short video clips of pedestrians. Each video clip consists of 200 frames, store as individual .png image files for easier frame-by-frame analysis. The naming convention for each clip follows the format vidfXY ZZZ.y, where Y and ZZZ are numerical identifiers for the video and frame numbers, respectively.

Key Features

• Grayscale Videos: Simplifies the task of detecting moving objects by eliminating color information, making it more suitable for testing motion-detection algorithms.
• Stationary Camera: Ensures that only the pedestrians are moving, enabling a focus on human behavior without other environmental distractions.
• Multiple Viewpoints: Although the initial release features only one scene, future expansions of the dataset promise diverse perspectives to enhance the dataset’s robustness.
• Clip-Base Organization: The videos are broken into 200-frame clips, allowing for manageable chunks of data that can be processed or analyze individually.
• Use in Dynamic Texture Modeling: This dataset has been historically important for studies in dynamic texture modeling, where pedestrian movement is treat as a time-varying texture for analysis.

Applications

The UCSD Pedestrian Database is widely use in:

• Pedestrian Detection: Developing algorithms that accurately identify pedestrians in video sequences.
• Crowd Behavior Analysis: Studying the collective movement and interactions of pedestrians in public spaces.
• Scene Segmentation: Separating pedestrians from the background to improve real-time video processing applications.
• Video Compression: Testing video encoding methods that can maintain quality while reducing file size.
• Surveillance Systems: Enhancing the accuracy of automate video surveillance for identifying unusual or suspicious pedestrian activity.