Abstract
Multiple object tracking based on tracking-by-detection is the most common method used in addressing illumination change and occlusion problems. In this paper, we present a tracking algorithm based on Edge Multi-channel Gradient Model. We first use the canny operator to extract the edges of the image, and establish a biologically inspired Multi-channel Gradient Model that integrate the spatio-temporal-spectral information of the edge to detect moving multiple objects. Under this model, the ORB feature is introduced to solve the problem of matching the object with the object library. Therefore, we can achieve object consistency, and the threshold classification method can solve the problem of multiple object occlusion in the process of persistent multiple object tracking. The experimental results show that the proposed method can effectively deal with the problems of occlusion and illumination changes. Compared with other state-of-the-art algorithms, the proposed algorithm achieves better performance on MOTA, MOTP, and IDF1. In particular, it performs best on IDSW on MOT2015 dataset, with an average improvement ratio of 28.99% over the second-place algorithm. In addition, our algorithm has a better performance in running time, achieving a good compromise between the speed and the accuracy.
Highlights
As an important task in computer vision, multiple object tracking (MOT) has extremely important applications in intelligent surveillance [1], [2], autonomous driving [3], medical diagnosis [4], and military vision guidance
- In our detection framework, we propose the Edge Multi-channel Gradient Model (E-McGM) model and use it for multiple object detection
E-McGM is evaluated on the image verification to demonstrate the performance of detection
Summary
As an important task in computer vision, multiple object tracking (MOT) has extremely important applications in intelligent surveillance [1], [2], autonomous driving [3], medical diagnosis [4], and military vision guidance. There are two types of occlusion, which are the occlusion of background objects and mutual occlusion between objects Illumination change is another common challenge in MOT. J. Chen et al.: Multiple Object Tracking Using Edge Multi-Channel Gradient Model With ORB Feature. A detector based on Edge Multi-channel Gradient Model (E-McGM) is used to detect the object bounding boxes in an image frame and combine ORB keypoints to achieve object consistency between multiple objects. To address the problem of incomplete object contour extraction in the process of multi-moving object detection, we use the E-McGM model to design a method for connecting the discontinuous edges of moving objects. - In our tracking framework, a new matching algorithm is proposed to improve the performance of matching between ORB and object library.
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