Segmentation Based Particle Filtering for Real-Time 2D Object Tracking

Abstract

Abstract. We address the problem of visual tracking of arbitrary objects that undergo significant scale and appearance changes. The classical tracking methods rely on the bounding box surrounding the target object. Regardless of the tracking approach, the use of bounding box quite often introduces background information. This information propagates in time and its accumulation quite often results in drift and tracking failure. This is particularly the case with the particle filtering approach that is often used for visual tracking. However, it always uses a bounding box around the object to compute features of the particle samples. Since this causes the drift, we propose to use segmentation for sampling. Relying on segmentation and computing the colour and gradient orientation histograms from these segmented particle samples allows the tracker to easily adapt to the object's deformations, occlusions, orientation, scale and appearance changes. We propose two particle sampling strategies based on segmentation. In the first, segmentation is done for every propagated particle sample, while in the second only the strongest particle sample is segmented. Depending on this decision there is obviously a trade-off between speed and performance. We perform an exhaustive quantitative evaluation on a number of challenging sequences and compare our method with the number of stateof-the-art methods previously evaluated on those sequences. The results we obtain outperform majority of the related work, both in terms of the performance and speed.

PixelTrack: A Fast Adaptive Algorithm for Tracking Non-rigid Objects

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A Robust Visual Tracking Method for Unmanned Mobile Systems

“…In order to improve the tracking result in an environment with noises and obstacles, researchers have made great effort in robust tracking algorithms. For example, Belagiannis et al tracked a moving car on the road by segmentation and color gradient orientation histograms [8]. Hua et al calculated the orientation and position of autonomous underwater vehicles by using a nonlinear visual servoing approach [9].…”

Tracking-by-Segmentation with Online Gradient Boosting Decision Tree

“…Aeschliman et al [1] propose pixel-level probabilistic models for joint target tracking and segmentation, but this algorithm assumes a static camera environment similar to a background subtraction. Another segmentationbased tracking algorithm is proposed in a particle filter framework by applying GrabCut [27] to each sample for robust observation [4], but the likelihood computation is based on simple features such as color and gradient distributions. A mid-level appearance structure, superpixel, is employed to distinguish the target from background, where target state is obtained by computing Maximum a Posteriori (MAP) based on a confidence map of superpixels [28].…”