Segmentation, Object detection, tracking, foundation models
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Smart Event Detection for Highlight Clips
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Prompted segmentation, motion tracking, event detection, video highlights
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Behaviour Lens
- Have you ever missed a highlight during a match? This system can capture highlights based on a user prompt or directly from a video.
- It uses advanced, state-of-the-art approaches, such as Meta’s SAM3, to track objects, detect events, and generate short highlight clips.
+ What if a video highlight came with the evidence behind it? Behaviour Lens takes a video and a text prompt, uses SAM3 to segment the requested object in sampled frames, and turns those detections into trajectories, velocities, timestamps, masks, overlays, and auditable CSV outputs.
- The goal is to combine modern segmentation models (such as SAM) with classical computer vision techniques. Segmentation serves as a strong perception layer, while event detection is driven by motion-based features such as trajectories, velocity, and frequency analysis, along with lightweight reasoning.
- The system follows a modular design, consisting of a general perception and feature extraction pipeline combined with task-specific event detection modules.
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- The system is primarily designed for human action detection (e.g., waving, raising a hand, standing up). As an extension, it can also handle simple sports scenarios, such as tracking a ball moving toward or crossing a goal, demonstrating its ability to generalize to multi-object interactions.
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+ On top of this traceable motion layer, the pipeline detects threshold events, ranks sustained motion windows, and handles multi-object car tracking for lane-change analysis. The final result is not just a short annotated clip, but a behaviour explanation that can be inspected frame by frame.
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