A Novel Target Algorithm based on TLD Combining with SLBP

doi:10.23940/ijpe.17.04.p13.458468

Abstract

Abstract:

TLD (Tracking-Learning-Detection) algorithm can be good for a long time to track the target in rotation, occlusion, illumination and other circumstances. However, in the case of uneven illumination, occlusion, tracking target fuzzy and so on, the problem of false tracking or tracking failure often occurs. Aiming at the shortcomings of TLD tracking algorithm, this paper will take TLD as the basic framework of target tracking and improve the detection module. When the tracking target has better texture feature, the SLBP (Semantic Local Binary Pattern) classifier is used to replace the nearest neighbor classifier in the detection module, which converts the image into SLBP texture feature vector to classify the samples using. In this paper, TLD-SLBP, MEEM, SCM, Struck and TLD are compared by using CVPR2013Benchmark test platform. The experiment results show that the TLD-SLBP algorithm has a higher success rate than other algorithms.

Submitted on February 3, 2017; Revised on April 22, 2017; Accepted on June 16, 2017
References: 14

Jitao Zhang, Aili Wang, Mingxiao Wang, and Yuji Iwahori. A Novel Target Algorithm based on TLD Combining with SLBP [J]. Int J Performability Eng, 2017, 13(4): 458-468.

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