Vehicle Detection, Tracking, and Speed Estimation Using Deep Learning and Computer Vision: An Application Perspective

Abstract

Proficient and precise item recognition has always been a prominent theme in the progression related to computer vision frameworks. Added further, due to the appearance of profound learning procedures, the exactness for object recognition has expanded manifoldly. The undertaking thus intends to be a part of a cutting-edge strategy for more precise object location with the sole objective of achieving high exactness coupled with a continuous presentation. In this present undertaking, we intend to utilize a profound learning-based methodology for object location in a start to finish design, using TensorFlow API to additionally take care of the issue of almost precise assessment of speed in moving vehicles. We surveyed on the idea of VASCAR, a prominent technical strategy, that police department frequently uses for estimating the speed of moving articles making use of distance accompanied by timestamps. We also studied the involvement of a human part, which prompts mistakes, and consequently, how our technique can efficiently address human blunder’s nature. Based on all this prior study, we then aptly chose to plan our own unique computer vision framework for gathering timestamps of vehicles for quantifying speed (when the distance is known). With the option of killing the human segment, our framework would be depending on our insight into material science and our product advancement aptitudes. Our framework depends on a mix of object recognition and item following to discover vehicles in a video transfer at various waypoints.