# An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments
## 3 Related Work
### Object Detection and Counting Using Traditional Approaches  
Object detection, tracking, and counting have been active research areas that gained more attention in the past few years. Some research has concentrated on building automatic detection and tracking systems that reduce the need for supervision. They frequently use background subtraction-based [11] moving object detectors because these detectors do not require a training stage or complicated system parameter setup. Several researchers have proposed and used versatile models to track, detect and count objects for various reasons. The authors have used models that use advanced probability concepts and ML approaches to detect, track, and count the objects effectively. However, these detectors have several flaws that result in false tracking, noisy and missing detections, and split and merged detections [12]. Furthermore, the relationship between detected and tracked objects is uncertain. Various data association approaches have been proposed to tackle these challenges. To represent possible split and merged occurrences, a collection of detected objects is augmented by virtual detections [13]. Some literature employs an overlapping criterion to ease the development of virtual detections [14].  
In [15], the authors proposed a probabilistic model for simulating split and merged detections, which uses a batch procedure to compute association hypotheses using the Markov Chain Monte Carlo method (MCMC). For a fixed number of objects, ref. [16] proposes a sequential technique based on MCMC sampling, while ref. [17] describes a similar strategy for a variable number of objects. The fundamental drawback of earlier methods is that they have a high computational cost and require specialized infrastructure to perform object detections in real-time. Other approaches [18; 19] restrict data association and tracking difficulties by using prior information about the geometry of the scene, such as the floor location and camera calibration. However, because the camera must be calibrated and the 3D plane of the floor must be estimated based on the camera position, this approach makes the system installation and setup difficult.  
For interacting targets, the authors [20] offer a multiple hypothesis tracker that generates split and merged data. The tracker is built on an efficient auxiliary variable particle filter based on Markov chain Monte Carlo (MCMC). Further investigations are needed to monitor the models' behavior to further improve the tracking efficiency and data association occurring during object interaction. The authors proposed a Bayesian Visual Surveillance Model that can handle unwanted measurements. Split and merged measurements, in particular, are explicitly characterized by stochastic processes. A particle filtering technique that blends traditional and MCMC sampling allows for reliable inference. The major problem associated with the authors' approach is the complexity associated with the Bayesian approach, due to which additional filters are used to combine traditional and MCMC sampling techniques, which improves the accuracy of simulating data association between Multiscale Residual (MR) and tracked objects.  
In [21], proposes a unique object detection and tracking model based on learning Existing techniques which operate by linearizing the motion that makes an implicit assumption about Euclidean space. The majority of computer vision transformations have a matrix Lie group structure. The learning model is expanded to train a class-specific tracking function, which is then combined with a pose-dependent object detector to create a pose-invariantobject identification system. When compared to the existing object identification approach, the suggested model can reliably recognize objects in various postures, while the search space is just a fraction of the size. The drawback associated with the authors' proposed approach is the cost associated with the experimental setup and more time consumption to initially train the model.