Multi-Level Fusion for Person Re-identification with Incomplete Marks

Most video surveillance suspect investigation systems rely on the videos taken in different camera views. Actually, besides the videos, in the investigation process, investigators also manually label some marks, which, albeit incomplete, can be quite accurate and helpful in identifying persons. This paper studies the problem of Person Re-identification with Incomplete Marks (PRIM), aiming at ranking the persons in the gallery according to both the videos and incomplete marks. This problem is solved by a multi-step fusion algorithm, which consists of three key steps: (i) The early fusing step exploits both visual features and marked attributes to predict a complete and precise attribute vector. (ii) Based on the statistical attribute d ominance and saliency phenomena, a dominance-saliency matching model is suggested for measuring the distance between attribute vectors. (iii) The gallery is ranked separately by using visual features and attribute vectors, and the overall ranking list is the result of a late fusion. Experiments conducted on VIPeR dataset have validated the effectiveness of the proposed method in all the three key steps. The results also show that through introducing marks, the retrieval accuracy is significantly improved.