Recently, the Kernelized Correlation Filters tracker (KCF) achieved competitive performance and robustness in visual object tracking. On the other hand, visual trackers are not typically used in multiple object tracking. In this paper, we investigate how a robust visual tracker like KCF can improve multiple object tracking. Since KCF is a fast tracker, many can be used in parallel and still result in fast tracking. We build a multiple object tracking system based on KCF and background subtraction. Background subtraction is applied to extract moving objects and get their scale and size in combination with KCF outputs, while KCF is used for data association and to handle fragmentation and occlusion problems. As a result, KCF and background subtraction help each other to take tracking decision at every frame. Sometimes KCF outputs are the most trustworthy (e.g. during occlusion), while in some other case, it is the background subtraction outputs. To validate the effectiveness of our system, the algorithm is demonstrated on four urban video recordings from a standard dataset. Results show that our method is competitive with state-of-the-art trackers even if we use a much simpler data association step.
@cite_9: Abstract This paper presents a real-time algorithm that allows robust tracking of multiple objects in complex environments. Foreground pixels are detected using luminance contrast and grouped into blobs. Blobs from two consecutive frames are matched creating the matching matrices. Tracking is performed using direct and inverse matching matrices. This method successfully solves blobs merging and splitting. Some application in automatic surveillance systems are suggested by linking trajectories and blob position information with the events to be detected.
@cite_3: In this paper, we present a new multiple hypothesestracking (MHT) approach. Our tracking method is suitablefor online applications, because it labels objects at everyframe and estimates the best computed trajectories up tothe current frame. In this work we address the problems ofobject merging and splitting (occlusions) and object fragmentations.Object fragmentation resulting from imperfectbackground subtraction can easily be confused with splittingobjects in a scene, especially in close range surveillanceapplications. This subject is not addressed in mostMHT methods. In this work, we propose a framework forMHT which distinguishes fragmentation and splitting usingtheir spatial and temporal characteristics and by generatinghypotheses only for splitting cases using observation inlater frames. This approach results in a more accurate dataassociation and a reduced size of the hypothesis graph. Ourtracking method is evaluated with various indoor videos.
Fuentes and Velastin @cite_9 proposed a method that performs simple data association via the overlap of foreground blobs between two frames. In addition to matching blobs based on overlap, Torabi @cite_3 validated the matches by comparing the histograms of the blobs and by verifying the data association over short time windows using a graph-based approach. These approaches track objects in a merge-split manner as objects are tracked as groups during occlusion.