Many adaptive tracking-by-detection methods have been proposed to track object with slowly changing appearance. However, most of those methods are designed for single object tracking. This paper proposes a method for adaptively tracking multiple objects based on a modified structured Support Vector Machine (SVM). The method utilizes the inter-object constraints and the layout information, which...

Abstract   In this paper, we propose a new method for kernel based object tracking which tracks the complete non rigid object. Definition the union image blob and mapping it to a new representation which we named as potential pixels matrix are the main part of tracking algorithm. The union image blob is constructed by expanding the previous object region based on the histogram feature. The pote...

Many contend that driving an automobile involves multiple-object tracking. At this point, no one has tested this idea, and it is unclear how multiple-object tracking would coordinate with the other activities involved in driving. To address some of the initial and most basic questions about multiple-object tracking while driving, we modified the tracking task for use in a driving simulator, cre...

This paper presents an object tracking system. Our goal is to create a real-time object tracker that can handle occlusions, track multiple objects that are rigid or deformable, and on indoor or outdoor sequences. This system is composed of two main modules: motion detection and object tracking. Motion detection is achieved using an improved Gaussian mixture model. Based on multiple hypothesis o...

Object tracking with multiple cameras is more efficient than tracking with one camera. In this paper, we propose a multiple-camera multiple-object tracking system that can track 3D object locations even when objects are occluded at cameras. Our system tracks objects and fuses data from multiple cameras by using the probability hypothesis density filter. This method avoids data association betwe...

Robust and accurate tracking of multiple objects is a key challenge in video surveillance. Tracking algorithms generally suffer from either one or more of the following problems, excluding detection errors. First, objects can be incorrectly interpreted as one of the other objects in the scene. Second, interactions between objects, such as occlusions, may cause tracking errors. Third, globally-o...