Semantic Person Search via Language-Driven Descriptions generated from CCTV cameras
Person search in real-world surveillance and robotic perception often fails when facial cues are unreliable due to low resolution, motion blur, or occlusion. We propose a clothing-centric person search framework that represents people using appearance attributes (e.g., garment type, dominant colors, accessories) instead of biometrics. Given person tracklets, the system samples frames, extracts clothing cues, and generates schema-constrained structured descriptions using a large language model, enabling consistent semantic indexing. Retrieval is performed with attribute-aware semantic search over these descriptions and compared against appearance-based baselines in controlled multi-camera experiments. A full-dataset analysis of the generated descriptions highlights common failure modes under real-world conditions, including color ambiguity under lighting changes (e.g., black vs dark), posture hallucinations under occlusion (e.g., standing behind a table described as sitting), and occasional prompt deviations that introduce spurious attributes. Runtime results show that detection is lightweight (about 30 ms per frame), while semantic extraction dominates; with parallel workers, the full pipeline processes roughly 0.25-0.34 s of computation per second of video, supporting practical deployment.