Detection and Tracking of Moving Targets for Thermal Infrared Video Sequences

• Published in: Sensors (Basel, Switzerland) Vol. 18; no. 11; p. 3944
• Main Authors: Li, Chenming, Wang, Wenguang
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 14-11-2018; MDPI AG
• Subjects: background subtraction; joint detection and tracking of multi-target; labeled random finite sets (RFS); thermal infrared (TIR) image; track-before-detect (TBD); δ-GLMB filter; δ-GLMB filter; track-before-detect (TBD); background subtraction; thermal infrared (TIR) image; labeled random finite sets (RFS); joint detection and tracking of multi-target
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s18113944

The joint detection and tracking of multiple targets from raw thermal infrared (TIR) image observations plays a significant role in the video surveillance field, and it has extensive applied foreground and practical value. In this paper, a novel multiple-target track-before-detect (TBD) method, which is based on background subtraction within the framework of labeled random finite sets (RFS) is presented. First, a background subtraction method based on a random selection strategy is exploited to obtain the foreground probability map from a TIR sequence. Second, in the foreground probability map, the probability of each pixel belonging to a target is calculated by non-overlapping multi-target likelihood. Finally, a δ generalized labeled multi-Bernoulli ( δ -GLMB) filter is employed to produce the states of multi-target along with their labels. Unlike other RFS-based filters, the proposed approach describes the target state by a pixel set instead of a single point. To meet the requirement of factual application, some extra procedures, including pixel sampling and update, target merging and splitting, and new birth target initialization, are incorporated into the algorithm. The experimental results show that the proposed method performs better in multi-target detection than six compared methods. Also, the method is effective for the continuous tracking of multi-targets.