Paper Info
Title
Multi-Target Multi-Camera Tracking with Human Body Part Semantic Features
Abstract
Recently, Multi-Target Multi-Camera Tracking (MTMCT) has gained more and more attention. It is a challenging task with major problems including occlusion, background clutter, poses and camera point of view variations. Compared to single camera tracking, which takes advantage of location information and strict time constraints, good appearance features are more important to MTMCT. This drives us to extract robust and discriminative features for MTMCT. We propose MTMCT\_HS which uses human body part semantic features to overcome the above challenges. We use a two-stream deep neural network to extract the global appearance features and human body part semantic maps separately, and employ aggregation operations to generate final features. We argue that these features are more suitable for affinity measurement, which can be seen as the average of appearance similarity weighted by the corresponding human body part similarity. Next, our tracker adopts a hierarchical correlation clustering algorithm, which combines targets' appearance feature similarity with motion correlation for data association. We validate the effectiveness of our MTMCT\_HS method by demonstrating its superiority over the state-of-the-art method on DukeMTMC benchmark. Experiments show that the extracted features with human body part semantics are more effective for MTMCT compared with the methods solely employing global appearance features.
Year
DOI
Venue
2019
10.1145/3357384.3358029
Proceedings of the 28th ACM International Conference on Information and Knowledge Management
Keywords
Field
DocType
feature fusion, hierarchical correlation clustering, multi-target multi-camera tracking
Data mining,Computer vision,Computer science,Multi camera tracking,Artificial intelligence
Conference
ISBN
Citations 
PageRank 
978-1-4503-6976-3
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Mingkun Wang100.34
Dian-Xi Shi210025.92
Naiyang Guan312.04
Yi Wei432.28
Tao Zhang522069.03
Zunlin Fan601.35