Two Target Tracking Algorithms: Motion Change and HSI Color Space Meanshift with Gaussian Weights
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In this article, we examine two distinct algorithms for target tracking applications. First, we explore the motion change algorithm, which tracks targets by detecting their movement patterns across consecutive frames in images or video sequences. This approach typically involves frame differencing techniques and movement vector calculations to establish target trajectories. Second, we present the meanshift algorithm utilizing HSI color space with Gaussian weighting, which employs color distribution characteristics for robust target tracking. This method involves histogram back-projection and iterative mean shift procedures to locate target positions based on color similarity metrics. Both algorithms possess unique characteristics and applicable scenarios - we will conduct an in-depth analysis of their advantages, limitations, and implementation considerations for selecting the appropriate algorithm based on specific application requirements.
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