Visual Tracking via Boolean Map Representations

 

Kaihua Zhang      Qingshan Liu       Ming-Hsuan Yang

 

Abstract


In this paper we present a simple yet effective Boolean map based representation that exploits connectivity cues for visual tracking. We describe a target object with histogram of oriented gradients and raw color features, of which each one is characterized by a set of Boolean maps generated by uniformly thresholding their values. The Boolean maps effectively encode multi-scale connectivity cues of the target with different granularities. The fine-grained Boolean maps capture spatially structural details that are effective for precise target localization while the coarse-grained ones encode global shape information that are robust to large target appearance variations. Finally, all the Boolean maps form together a robust representation that can be approximated by an explicit feature map of the intersection kernel, which is fed into a logistic regression classifier with online update, and the target location is estimated within a particle filter framework. The proposed representation scheme is computationally efficient and facilitates achieving favorable performance in terms of accuracy and robustness against the state-of-the-art tracking methods on a large benchmark dataset of 50 image sequences.

 

Boolean Map Representation


 

Figure 1. Boolean map representation. For clarity, only the BMRs of a positive and negative sample are demonstrated. Note that the Boolean maps contain more connected structures than the LAB+HOG representations.

 

Experimental Results



Datasets: For experimental validation, we use the tracking benchmark dataset and code library [7] which includes 29 trackers and 50 fully-annotated videos (more than 29,000 frames). In addition, we also add the corresponding results of 6 most recent trackers including DLT [35], DSST [36], KCF [15], TGPR [37], MEEM [5], and HCF [23].

Figure 2 shows the success and precision plots of OPE, TRE and SRE of the top 10 performing tracking methods. Overall, the proposed BMR algorithm performs well against the state-of-the-art tracking methods. We analyze the tracking performance based on 11 challenging attributes of object tracking [7]. Overall, the BMR method performs well in all attributebased evaluation against the state-of-the-art methods.

 

Figure 2 - The success plots and precision plots of OPE,TRE and SRE for the top 10 trackers. The performance score of success plot is the AUC value while the performance score for each tracker is shown in the legend. The performance score of precession plot is at error threshold of 20 pixels.

 

 

Related Publication


Visual Tracking via Boolean Map Representations
Kaihua Zhang, Qingshan Liu, Ming-Hsuan Yang.
submitted to TIP
[Code](coming soon)

Video Tracking Results


We show tracking results of 50 challenging videos.