Particle Filter Vehicles Tracking by Fusing Multiple Features

Abstract

Real-time and accurate vehicle tracking by Cameras and Surveillance can provide strong support for the acquisition and application of important traffic parameters, which is the basis of the traffic condition evaluation and the reasonable traffic command and dispatch. To deal with difficult problems of vehicle tracking research in a complex environments, such as occlusion, sudden illumination change, similar target interference and real-time tracking, measures are taken as follows. Firstly, the existing color local entropy particle filter tracking method is improved. The symmetry of information entropy is used to overcome the tracking failure caused by large-area occlusion. Secondly, the SIFT feature tracking method is improved to enhance real-time performance and robustness. Thirdly, two tracking methods were combined according to their characteristics, aiming at effectively improving the quasi-determination and real-time performance of vehicle tracking. Fourthly, Kalman filter was used to predict the motion state of vehicles. According to the SIFT characteristics and license plate information of vehicles, the exact position of the lost target vehicles is quickly located. It has been verified by experiments that our method has effectively improved the accuracy and real-time performance of vehicle tracking in complex situations.INDEX TERMS Particle filter, vehicle tracking, color local entropy, scale-invariant feature transform (SIFT), symmetry.

Particle Filter Based Video Object Tracking Scheme With Target Remodeling and Reinitialization and its Hardware Implementation Using Raspberry Pi

“…Further, target modeling is accomplished by the fusion of color spectra and thermal information [37] and also by adaptively fusing the color and texture features [8,24]. In order to further improve the tracking accuracy, Wang et al [31] have considered the color and local entropy feature fusion to model the target. As far as the issue of illumination variation is concerned, color and Harris corner features are fused by Lu et al [38] for target modeling and the tracking accuracy is further improved by considering the fused feature of color and EOH [39].…”

“…Hence, feature based particle filter algorithms [7][8][9] are preferred to other algorithms. Further, it has been reported in the literature that single feature based object models in particle filters such as color feature based particle filters [22][23][24], edge feature based particle filter [25], motion feature based particle filter [26][27][28], appearance feature based particle filter [29,30], entropy feature based particle filter [31,32] could handle one or two typical issues of the scene during tracking and have achieved good tracking accuracy in initial frames.…”

Multi-Channel Feature Dimension Adaption for Correlation Tracking

“…It is a task aiming to continually detect a target in a video sequence only its initial position is given. It has numerous realworld applications, including vehicle tracking [4], automatic surveillance [5], and pedestrian tracking [6], [7]. However, it is suffering from some challenging visual attributes, such as background clutters, occlusions [8], motion changes, and size changes.…”

Efficient Online Tracking-by-Detection With Kalman Filter

“…Among the various on-line tracking-by-detection methods, Use of probabilistic models [29], [30], particle filters [31], [32] and probability hypothesis density (PHD) filters [33]- [36] have been extensively discussed. Instead of relying on probabilistic models, the IOU tracker [27] achieves high speed by using only detection bounding boxes as the input while delivering performance improvements over previous optimization-based approaches.…”