| Summary: | 碩士 === 國立臺灣師範大學 === 資訊工程學系 === 104 === Human detection and tracking has been an important research topic in recent years. Most commonly used methods of human tracking either require a large amount of computing time to reach high accuracy, or sacrifice the accuracy to obtain rapid tracking results. To date, there is no algorithm which can fast and accurately detect and track pedestrians. In light of this, the current study employed the Particle Swarm Optimization (PSO) algorithm with Color Histogram methods, aimed to develop an optimized algorithm for human detection and tracking. The proposed algorithm include four steps: 1) Histogram of Oriented Gradient (HOG) was used to detect the locations of pedestrians; 2) Image Pre-processing was used to remove the influence of light and shadow; 3) Color Histogram of the image was computed to calculate the similarity of feature points; and 4) Fitness Function of PSO algorithm was computed to proceed Human tracking. To examine the speed and accuracy of the proposed algorithm, images with pedestrians in different moving ways (Lateral movement, randomize movement and depth movement) were collected from seven image databases. The findings suggest that compare to other algorithms, the PSO algorithm can reach similar or better accuracy (Multiple Object Tracking Accuracy, MOTA：69.88 ~ 86.54%；Multiple Object Tracking Precision, MOTP：77.43 ~ 84.76%) in shorter computing time (0.0784 ~ 0.0906s). Even if the image data were interfered by partial or entire occlusion, the tracking accuracy of the PSO algorithm can still reach 80% or above. The findings reveal that compared with other existed algorithms, the PSO algorithm not only significantly reduces the computing time, but also can reach excellent tracking accuracy. Future studies are warranted to improve the PSO algorithm tacking accuracy when process image data with partial and entire occlusion.
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