# An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments
## 3 Related Work
### Object Detection and Counting Using Deep Learning Techniques
The first two categories are for object detection and the last two are for object tracking. The first category is object detection in images, where the job is to detect objects of a given set of object classes (e.g., vehicles and pedestrians) from individual photographs obtained by any source. In the second category, objects are detected from the videos collected from various sources. Object tracking is done based on two methods, the first method is single tracking where the goal of the work is to estimate the condition of a target, which is indicated in the first frame, in real-time across frames. The second method is muti object tracking where the goal of the task is to recover object trajectories in each video frame using prior detection or without prior detection. Object tracking algorithms are extremely useful in many applications, they can be divided into four main categories [33], matching-based tracking; filtering-based tracking, class-based tracking, and fusion-based tracking. More recently, several deep learning methods are combined with other algorithms to improve tracking performance [34]. Object Counting based on deep learning methods is an active research area that attracted many scholars in recent years. The authors in [35] developed object counting algorithms to count vehicles using the Single Shot Detector (SSD) algorithm. Again, the cost to establish the infrastructure and train the model is complicated and expensive. Further test time detection associated with the model is slow. Some tried to establish an object counting quantitative comparison between background subtraction, Viola-Jones, and Deep Learning Methods on four different datasets [36]. Others investigated YOLO's potential in object counting. One category of researchers build a traffic counting system based on YOLO. They have utilized simple distance calculations to achieve the purpose of vehicle counting, and they added checkpoints to alleviate the consequence of false detection [37; 38]. Similarly, other researchers used YOLO as a primary object detector, and they have combined it with correlation filters to build an object counting system [39]. Correlations filters correlate two samples to find the similarity between them. Once an object is detected, they start tracking it until it gets out of the frame or disappears[PERSON] However, this is a computationally expensive algorithm since it tracks every object in the frame. Moreover, it is more vulnerable to tracking failures as objects that disappear and reappear might be counted twice. Finally, to avoid counting the same object twice, and to reduce the computational complexity, ref. [40] suggested detecting objects every N frame, then using Kanade-Lucas-Tomasi feature tracker (KLT) to track counted objects. [PERSON] et al. [41] introduced a novel intelligent real-time traffic monitoring system. A filtered You Only Look Once (YOLO) is [PERSON] to achieve vehicle detection. The YOLO framework has been pre-trained to recognize 80 items. The suggested technology is being tested on three different types of vehicles: buses, trucks, and cars. After extracting the three groups, a checkpoint is assigned to determine the number of vehicles in each lane. The count is utilized to manage the road traffic signal in real-time. Three separate publicly available traffic movies are used to evaluate the system. The authors have used Kernel Correlation Filter (KCF) along with the YOLO model for object detection and counting. The inclusion of the KCF filter has improved the system's accuracy. However, the model misclassified between the trucks and trains, which affected the accuracy of object detection and counting. (Object Detection Using YOLO Framework for Intelligent Traffic Monitoring). [PERSON] et al. [42] have provided a method for detecting and counting vehicles in mixed traffic flows using YOLOv4 in the context of Vietnamese transportation. Motorcycles, bicycles, automobiles, trucks, and buses were among the five vehicle types tested on the network.