## 1 Introduction  
Multi-person tracking is currently one of the most essential and challenging research topics in the computer vision community [1, 2, 3, 4, 5, 6, 7, 8, 9]. Because of the common availability of high-quality low-cost video cameras and considering the inefficiency of manual surveillance and monitoring systems, automated video surveillance is now essential for today's crowded and complex environments. To monitor, control, and protect crowds, accurate information about numbers plays a vital role in operational and security efficiencies [10; 11; 12; 13; 14; 15; 16]. The counting and tracking of many persons is a challenging problem [17; 18; 19; 20; 21; 22; 23; 24; 25] due to occlusions, the constant displacement of people, different perspectives and behaviors, varying illumination levels, and because, as the crowd gets bigger, the allocation of pixels per person decreases. A primary concern in surveillance and monitoring systems is to identify human crowd behaviors and supervise the crowd to prevent disasters and unforeseen events [26; 27; 28; 29; 30; 31; 32; 33; 34]. The analysis of human behavior in crowded scenes is one of the most important and challenging areas in current research [35; 36; 37; 38; 39; 40; 41; 42; 43]. Traditional visual surveillance systems that depend purely on manpower to analyze videos is inefficient because of the enormous number of cameras and screens that require monitoring, human fatigue due to time spent on lengthy monitoring periods, paucity of essential fore-knowledge and training in what to look for, and also because of the colossal amount of video data that is generated per day. Such issues necessitate an automated visual surveillance system that can reliably detect, isolate, analyze, identify, and alert responders to unusual events in real time. Automated surveillance systems seek to detect human behaviors automatically in crowded scenes, and it has many potential applications, such as security, care of the elderly and infirm, traffic monitoring, inspection tasks, military applications, robotic vision, sports analysis, video surveillance, and pedestrian traffic monitoring [44; 45; 46; 47; 48; 49; 50; 51; 52]. In this research article, we propose a robust new particles-based approach for multi-person counting and tracking, which addresses the problematic fact that, as the density of a crowd increases, the number of pixels allocated per human decreases. By using our particles-based approach, we were able to count and track multiple persons in crowded scenes and efficiently deal with occlusions, arbitrary movements, and overlaps. We also propose a new approach for crowd behavior detection using an improved entropy classifier based on the fusion of global and local descriptors extraction. First of all, we applied pre-processing steps on extracted video frames for noise removal, edge detection, and contrast adjustment, then human/non-human detection was performed using multi-level thresholding and morphological operations. We applied a distance algorithm for human silhouette extraction. After that, our work involved two facets: (i) multi-people tracking and (ii) crowd behavior detection. In the multi-person tracking phase, we first verified the extracted silhouettes by a particles force model, then we converted extracted foreground objects into particles, and, using physics phenomena of the mutually interacting particles force model, non-human objects were discarded. As every extracted human silhouette is a collection of particles, by treating groups of particles that make one silhouette as a cluster, we performed labeling and cluster estimation using a K-nearest neighbors search algorithm to count the persons. We then fixed the human silhouettes with a unique integer ID, and, using normalized cross correlation as a cost function and the Jaccard similarity index, multi-person tracking was performed.