multiple vehicle tracking using camshift matlab code
#1

hi am jancy.i would like to get the matlab code for multiple object tracking not using kalman filter
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#2
Cameras and video technology have become an integral part of our daily lives. Surveillance is an area that has benefited greatly from video technologies. This in turn increases the need for automatic video surveillance algorithms that can track objects and raise the alarm if necessary. Tracing people is one of those areas. On the other hand, CAMSHIFT is a tracking algorithm that has been widely applied in the tracking of faces in the past. However, it has not been used in vehicle tracking. Therefore, this document presents a modified CAMSHIFT algorithm that can be used in tracking vehicles in video sequence. This will be covered in two ways; Detection of moving objects of interest from frame to frame and then evaluate the performance of our modified CAMSHIFT algorithm in different video sequences. The aim of this research is to develop suitable methods for automatic visual surveillance of traffic to perform detection, Vehicles in Real time as well as dealing with changes in ambient lighting and vehicle occlusion. Each of the detected vehicles is assigned a cam shift tracker that can quickly track objects of different sizes and shapes. The experimental results of the traffic scenes demonstrate the efficiency and robustness of the methods.


Video cameras play an important role in achieving the potentials promised by Intelligent Transportation Systems. In particular, video tracking systems have been widely applied in the collection of traffic flow and incident detection data by tracking and analyzing vehicle trajectories through video image processing. In this work we propose an improved adaptive continuous change method (CAMShift) for the automatic tracking of multiple vehicles using video cameras. It focuses on current vehicle tracking methods that are limited to complex traffic scenes such as non-destination exclusivity and colour distraction in real traffic situations. The proposed method first uses a background subtraction method to automatically detect, select and initialize targets, ie vehicles. In addition, a motion estimation method is proposed to estimate vehicle motion states and predict new vehicle locations. Finally, both the colour and edge distributions of the vehicles are extracted and a mixed model is established to search for matching vehicles in the following image box. The proposed method was comparatively evaluated with the traditional CAMShift method using the same video sequences captured in real traffic situations. The results of the evaluation showed that the proposed method is efficient for precise tracking of vehicle movements and is promising for practical applications.
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