# Multi-Pedestrian Tracking Based on KC-YOLO Detection and Identity Validity Discrimination Module
## 2 Related Work  
### Target Detection Methods  
Target detection serves as the foundational component in the domain of multi-target tracking. The role of the detector is to furnish the tracker with the positional information of objects within the image, typically yielding the detection frame of the object. Presently, target detection algorithms achieving high accuracy are frequently implemented on the bedrock of Deep Convolutional Neural Networks (CNNs) [21]. Unlike traditional methods, deep-learning-based object recognition utilizes CNNs to autonomously capture recognizable object features. This automatic extraction process allows the model to learn complex patterns and representations from the input data, thereby improving the recognition accuracy and efficiency. In addition, hierarchical learning using CNNs allows the model to recognize features at different levels of abstraction, resulting in more justifiable and precise feature extraction. It has diverged based on detection principles, segregating into two types of methodologies [7].  
Two-stage target detection involves generating candidate regions and subjecting them to a two-fold classification process. Region proposals utilizing a CNN (R-CNN) [22] input fixed-size images into a neural network to facilitate training and object feature extraction. While it attains higher detection precision compared to traditional object-detection methods, it does suffer from computational intensity and LATiness in object detection. Extending from the R-CNN algorithm, Fast R-CNN and Faster R-CNN emerge. Although these methods enhance detection accuracy compared to traditional approaches, the bifurcation between candidate region generation and classification engenders sluggish algorithmic operation, hampering real-time target detection realization. Efforts to enhance real-time capabilities still grapple with the challenge of duplicated computation. Additionally, R-CNN is hamstrung by its fixed input image size. To mitigate the pre-input image-scaling computational burden, the Spatial Pyramid Pool Network (SPPNet) was conceived, albeit only partially reducing superfluous computations. Among the R-CNN family, Faster R-CNN currently stands out with the swiftest and closest-to-real-time detection performance. This efficiency is pivotal for applications demanding rapid and accurate object detection, yet it remains encumbered in meeting the demands of intricate target detection scenarios.  
One-stage detection algorithms eschew the region proposal phase and promptly produce class probabilities and the positional coordinates of objects. Representative algorithms include the YOLO family [23], the Single-Shot Multi-Frame Detector (SSD), and RetinaNet. YOLO's framework implements the detection process by allowing the model to directly predict the bounding box and class probability of each cell, which distinguishes this method from two-stage object-detection models. By doing so, YOLO achieves a more efficient and faster differentiation and correlation process, making it particularly suitable for real-timeapplications such as video analysis and object tracking. Notwithstanding its strengths, YOLO demonstrates suboptimal detection accuracy for smaller objects. SSD capitalizes on feature maps of varying dimensions for object detection, rectifying YOLO's shortcomings in smaller-object detection. The contemporary YOLO family of algorithms collectively refines the detection accuracy without compromising on high detection speed.  
However, in intricate traffic environments, existing object-detection algorithms still cannot simultaneously ensure real-time performance and capture as many feature points as possible.