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Object detection and tracking are vital in computer vision and visual surveillance, allowing for the detection, recognition, and subsequent tracking of objects within images or video sequences. These tasks underpin surveillance systems, facilitating automatic video annotation, identification of significant events, and detection of abnormal activities. However, detecting and tracking small objects introduce significant challenges within computer vision due to their subtle appearance and limited distinguishing features, which results in a scarcity of crucial information. This deficit complicates the tracking process, often leading to diminished efficiency and accuracy. To shed light on the intricacies of small object detection and tracking, we undertook a comprehensive review of the existing methods in this area, categorizing them from various perspectives. We also presented an overview of available datasets specifically curated for small object detection and tracking, aiming to inform and benefit future research in this domain. We further delineated the most widely used evaluation metrics for assessing the performance of small object detection and tracking techniques. Finally, we examined the present challenges within this field and discussed prospective future trends. By tackling these issues and leveraging upcoming trends, we aim to push forward the boundaries in small object detection and tracking, thereby augmenting the functionality of surveillance systems and broadening their real-world applicability.

Vector quantization (VQ) is a lossy compressiontechnique that mainly includes three stages: codebook generation, encoding and decoding. The efficiency of VQ extremely depends on the achieved codebook quality. The most commonly used method for VQ codebook generation, is the Linde-Buzo-Gray (LBG) algorithm. High sensitivity to initial codebook, is mentioned as one of the drawbacks of LBG algorithm. An effective codebook initialization technique in LBG algorithm has been proposed in this paper. The subtractive clustering has been employed to generate a proper initial codebook. Experimental results show that compared with other methods like common LBG algorithm and cluster density method, less RMSE and higher PSNR, is achieved owing to use presented method.

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