International Core Journal of Engineering 2020-26 | Page 77

2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM) An Improved Meanshift Tracking Algorithm Using Adaptive Quantization step in Color Space Chao Zhang Yunfeng Zhang Xiangping Gao Bing Cheng School of Computer Science and Technology Anhui University Hefei, Anhui, China [email protected] School of Computer Science and Technology Anhui University Hefei, Anhui, China [email protected] School of Computer Science and Technology Anhui University Hefei, Anhui, China [email protected] School of Computer Science and Technology Anhui University Hefei, Anhui, China [email protected] meanshift tracking algorithm is to find the local extrema of the probability density function by quick iteration in the direction of the gradient ascent to generate the mean shift vector and determine the position of the target in the current frame by model matching. To reduce the computational complexity of the algorithm in practical application, a method of restricted searching area is usually used so that the current search is performed only in the vicinity of the previous target position. Abstract—The traditional meanshift based tracking algorithm uses a constant quantization step to carry out feature generation in the color space but it cannot dynamically alter the quantization step with the changes of the target geometry to improve computational efficiency in large depth-of-field scenarios. Based on the traditional meanshift algorithm, we proposed a tracking algorithm using adaptive quantization step which automatically adjusts the quantization step of the color histogram and uses the dynamic time warping algorithm to match the features with different dimensions when the target geometry changes, thereby, effectively reducing the average frame processing time. The comparative experiments under multiple scenarios demonstrated that the proposed algorithm can adaptively adjust the quantization step of color histogram in large depth of field scenarios and improve the operating efficiency of the algorithm. This paper tries to improve the operating efficiency of meanshift based tracking algorithm from another perspective. In the proposed algorithm, the quantization step of the color histogram will be automatically adjusted when the target size drastically changes in large depth-of-field scenarios. Meanwhile, dynamic time warping (DTW) algorithm is introduced to match the features of different dimensions. The use of dynamic quantization steps enables the improved algorithm to extract target information in a more efficient way and reduce the computational load. The rest of this paper is organized as follows: in Section 2, an improved meanshift algorithm with adaptive quantization steps is given and DTW is introduced to match the feature vectors with different dimension. The experimental results in different scenarios are presented and discussed in Section 3, which demonstrate the efficiency improvement due to the use of adaptive quantization steps. Section 4 concludes the paper. Keywords—Target tracking, meanshift, adaptive quantization step, model matching I. I NTRODUCTION As an integral part of the wildly used intelligent video systems [1-5], target tracking is the basis for various high-level intelligent analysis and processes in intelligent video systems. Basically, the tracking of moving target refers to the scene where the effective features of the target and appropriate matching algorithms are employed to find the regions that are most similar to the original target in the image sequence. In practical applications, not only can the tracking algorithm provide the target's trajectory and accurate position information, but also provide effective information for behavior understanding and decision-making through the analysis of target’s moving speed and direction. Nam H and Han B proposed a novel visual tracking algorithm based on the representations from a discriminatively trained Convolutional Neural Network [6]. However, limited by the computational capabilities, few systems are able to continuously perform more advanced processions such as real-time target recognition and behavior analysis after completing the multi-channel tracking. Therefore, how to reduce the time complexity of the tracking algorithm in practice has become the focused issue all this time. II. A DAPTIVE Q UANTIZATION S TEPS M EANSHIFT A LGORITHM IN C OLOR S PACE FOR T ARGET T RACKING The meanshift algorithm is a non-parametric method that describes the distribution of pixel values based on a specific kernel function and then iteratively searches the local extrema of the kernel function. The color histogram is usually employed to describe the target, and Bhattacharyya coefficients are used to measure the similarity between the target model and the candidate one. For each frame of the video, the color histogram of the target area is calculated using selected kernel function and the area with the greatest similarity to the target model in the candidate area of the next frame is the position of the target in the next frame. The traditional meanshift tracking algorithm uses constant quantization steps of color histogram for the generation of both target and candidate models, which ignores the dynamic change of color information due to changes in the target spatial position. The prototypical meanshift algorithm was originally used by Fukunaga et al. [7] in cluster analysis and soon afterwards in image processing by Cheng et al. [8] and then applied to the tasks of image segmentation and target tracking by Comaniciu and Meer et al. [9-10]. The essence of the 978-1-7281-4691-1/19/$31.00 ©2019 IEEE DOI 10.1109/AIAM48774.2019.00018 55