Object tracking and counting in a zone using YOLOv4, DeepSORT and TensorFlow

Abstract

The main objective of this research work is to solve multiple object tracking problems in a given frame, wherein the proposed model intends to identify and track various objects. The problem has been solved in three stages viz. detecting, identifying, and tracking the object in a particular zone, i.e., but it is observed that something more could be done in this field, mostly the MOT-A score was not up to the mark; hence the proposed research work utilized Kalman filters for obtaining enhanced results and compared the obtained MOT-A metric with previous works, and the results were good. Object detection and recognition occur via the YOLO algorithm, which enables us to classify the objects into 80 classes. Then, Motion Prediction and feature generation occur in which an estimation model is created, and Kalman filters are used to model these states for capturing moving objects in the frame. Finally, tracking takes place with Kalman filters in the previous frame, and newly detected objects are placed in the current frame, after which an association is made for new detection. All this is done via the DeepSORT algorithm, which is essentially a Deep association metric with the SORT algorithm. Here, Kalman filters are used as they improved the accuracy of the proposed model and yielded better results. On the same lines, YOLO is used to perform object detection and recognition at the same time. It is also a detector, which by applying a single neural network, it can predict the bounding boxes and perform multi-class classification. This problem can have various applications, especially in traffic management. It can also prevent people from gathering during COVID times and raising an alert for all the authorities. Hence, this is a multidisciplinary approach wherein the work of object detection is being used in various fields like crowd assembling, Surveillance, Animal management in Zoo/Biodiversity parks as well as in the case of traffic systems as well. The real motivation behind this work was using the state-of-the-art technology to solve the modern-day problems. Manually human vigilance in large areas is a utopian task and especially when surveillance and security is big threat out there. Adding to it is the COVID Pandemic which has claimed millions of lives and yet vaccination is a still a dream yet to be realized. Hence, cluster identification problem is considered and also the ways to solve this challenge using the state-of-the-art technology. Once it has been achieved, it is initiated further to find more applications of this technology in various other domains and found out how it is applied in various use cases in case of crowd gathering at a single point and how this is a similar problem in case of animal gathering at a point and how forest rangers can solve the problem in a more efficient way.

Topics & Concepts