PRISM: Person Re-Identification via Structured Matching
This work addresses the problem of tracking individuals across camera networks for surveillance applications, representing an incremental improvement over existing methods.
The paper tackles the problem of person re-identification in visual surveillance by addressing challenges like pose and illumination changes, proposing PRISM, a structured matching method that uniformly outperforms state-of-the-art methods in matching rate while being computationally efficient.
Person re-identification (re-id), an emerging problem in visual surveillance, deals with maintaining entities of individuals whilst they traverse various locations surveilled by a camera network. From a visual perspective re-id is challenging due to significant changes in visual appearance of individuals in cameras with different pose, illumination and calibration. Globally the challenge arises from the need to maintain structurally consistent matches among all the individual entities across different camera views. We propose PRISM, a structured matching method to jointly account for these challenges. We view the global problem as a weighted graph matching problem and estimate edge weights by learning to predict them based on the co-occurrences of visual patterns in the training examples. These co-occurrence based scores in turn account for appearance changes by inferring likely and unlikely visual co-occurrences appearing in training instances. We implement PRISM on single shot and multi-shot scenarios. PRISM uniformly outperforms state-of-the-art in terms of matching rate while being computationally efficient.