Robust Superpixel Tracking with Weighted Multiple-Instance Learning

Abstract

SUMMARYThis paper proposes a robust superpixel-based tracker via multiple-instance learning, which exploits the importance of instances and mid-level features captured by superpixels for object tracking. We first present a superpixels-based appearance model, which is able to compute the confidences of the object and background. Most importantly, we introduce the sample importance into multiple-instance learning (MIL) procedure to improve the performance of tracking. The importance for each instance in the positive bag is defined by accumulating the confidence of all the pixels within the corresponding instance. Furthermore, our tracker can help recover the object from the drifting scene using the appearance model based on superpixels when the drift occurs. We retain the first (k − 1) frames' information during the updating process to alleviate drift to some extent. To evaluate the effectiveness of the proposed tracker, six video sequences of different challenging situations are tested. The comparison results demonstrate that the proposed tracker has more robust and accurate performance than six ones representing the state-of-the-art.

An Adaptive Superpixel Tracker Using Multiple Features

“…The sequences chosen for evaluating the algorithms contain challenges like occlusion, the change of illumination and large movement of the target. The compared algorithms include IVT [Ross, Lim, Lin et al (2008)], VTD [Kwon and Lee (2010)], MIL [Babenko, Yang and Belongie (2009)], L1 [Bao, Wu, Ling et al (2012)], TLD [Kalal, Mikolajczyk and Matas (2012)], Frag [Adam, Rivlin and Shimshoni (2006)], SPT [Yang, Lu and Yang (2014)], and Cheng [Cheng (2015)], whose code can be obtained from the authors' homepage. Then, for completeness of the analysis, the proposed tracker is performed on the recent dataset VOT2014 [Kristan, Leonardis, Matas et al (2015)], which contains 25 sequences.…”

“…Concerning to estimate the target position, Junqiu employs multiple hypotheses for superpixel matching and projects the matching results onto a displacement confidence map [Wang and Yagi (2014)]. In consideration of recovering the object from the drifting scene, Xu proposes robust superpixel tracking with weighted multipleinstance learning which achieves robust and accurate performance [Cheng (2015)]. Apart from making efforts on the discriminative appearance model, Yuxia proposes a superpixel tracking method via graph-based hybrid discriminative-generative appearance model to deal with occlusion and shape deformation [Wang and Zhao (2015)].…”

Object Tracking with Embedded Deformable Parts in Dynamic Conditional Random Fields

“…On top of that, DPMs consist of a mixture of components to describe different poses and perspectives of target. Tracking target by object recognition and detection methods has been proven as a promising way by researchers [4], [5]. Differing from previous work, our work attempts to introduce prior knowledge of target at the beginning of tracking, while others focus on updating the target model iteratively by on-line learning methods.…”