# Multi-Object Tracking with Grayscale Spatial-Temporal Features
## 1 Introduction  
Multiple object tracking (MOT) is an important research field in computer vision, and it is also an integral part of cutting-edge technologies such as action recognition. Recently, MOT has achieved a wide range of applications in artificial intelligence, such as autonomous driving, intelligent traffic management, and video monitoring. It aims to detect the target of interest, assign each target a separate ID, maintain target identities, and generate their respective trajectories throughout the entire video stream. With the development of MOT algorithms, it is still a challenge to improve the accuracy and speed, as the overlap matching between many objects can cause the target detection to fail or slow. In early computer vision, tracking relied on the features of interest points, which was simple and fast but poorly robust. In recent years, deep learning has developed rapidly and has been widely used in the image field. With the continuous iteration of YOLO [1], Faster R-CNN [2], and other models [3], the speed and accuracy of the target detection algorithm have been significantly improved. So much MOT research is based on tracking by detection (TBD) [4]. However, the TBD model not only takes a long time for target detection but also requires some calculation time for feature extraction and ID matching. In order to solve this problem, a new model that joins detection and embedding (JDE) [5] is proposed. Although the JDE model reduces the amount of calculation compared to the TBD model, it only combines the two parts of target detection and feature extraction, so the complexity of the model is still high. Recently, a new model named joint detection and tracking (JDT) [6] was proposed by combining target detection, feature extraction, and data association in a single network, which simplifies tracking. But the association strategy stillhas a lot of complexity and calculation, as each target needs to be matched multiple times. Many JDT models use spatial-temporal features; however, their use of neural networks to extract spatial-temporal features requires extensive computation. Today, many monitoring devices do not have high computing power and do not support real-time tracking through neural networks. But most of the popular methods today are implemented using deep learning. Therefore, there are two problems to be solved in this paper. The first is how to quickly obtain spatial-temporal features in the traditional way. The second is how to reduce unnecessary feature extraction and matching. In order to solve these problems, we propose a new method to obtain spatial-temporal features, which is convenient only based on grayscale maps. The proposed spatial-temporal feature is called the grayscale spatial-temporal feature. This spatial-temporal feature marks each target with different grayscale values in each frame, which can quickly obtain the spatial-temporal features of the target. At the same time, the matching of grayscale spatial-temporal features is also convenient and fast. We also optimize the association strategy by joint feature extraction and matching to reduce unnecessary feature extraction. Our feature extraction and matching operations are performed in a traditional way without deep learning. In this paper, we extract motion features through the Kalman filter algorithm and predict the target position of the next frame through the state equation and the target position of the historical frame. However, the Kalman filter algorithm is only applicable to linear motion, so it is necessary to extract appearance features to increase the robustness of tracking. The color histogram is used to obtain the appearance features. The color histogram can intuitively reflect the color distribution and color characteristics of the image. The color histogram is more efficient than the scale-invariant feature transform (SIFT) in extracting appearance features and is simpler to calculate.