Robust compressive tracking via online weighted multiple instance learning
This work addresses robust object tracking for computer vision applications, but it is incremental as it builds on existing sparse representation and multiple instance learning methods.
The paper tackled the problem of robust visual object tracking under challenges like occlusion and motion blur by integrating a coarse-to-fine search strategy based on sparse representation with weighted multiple instance learning, achieving improved accuracy and efficiency as shown in experiments on benchmark datasets.
Developing a robust object tracker is a challenging task due to factors such as occlusion, motion blur, fast motion, illumination variations, rotation, background clutter, low resolution and deformation across the frames. In the literature, lots of good approaches based on sparse representation have already been presented to tackle the above problems. However, most of the algorithms do not focus on the learning of sparse representation. They only consider the modeling of target appearance and therefore drift away from the target with the imprecise training samples. By considering all the above factors in mind, we have proposed a visual object tracking algorithm by integrating a coarse-to-fine search strategy based on sparse representation and the weighted multiple instance learning (WMIL) algorithm. Compared with the other trackers, our approach has more information of the original signal with less complexity due to the coarse-to-fine search method, and also has weights for important samples. Thus, it can easily discriminate the background features from the foreground. Furthermore, we have also selected the samples from the un-occluded sub-regions to efficiently develop the strong classifier. As a consequence, a stable and robust object tracker is achieved to tackle all the aforementioned problems. Experimental results with quantitative as well as qualitative analysis on challenging benchmark datasets show the accuracy and efficiency of our method.