| Title |
Vision-based Detection, Tracking, and Classification of Vehicles |
| DOI |
https://doi.org/10.5573/IEIESPC.2020.9.6.427 |
| Keywords |
Video analysis; Adaptive background estimation; Traffic Planning; Traffic counting; Turning movement counts |
| Abstract |
Motion capturing involves the identification of objects in motion and plotting their motion by analyzing them. When considered in regard to a video sequence, motion capturing can be defined as a procedure of unmasking objects in motion in sequences of frames using exhaustive and proficient digital image processing techniques. Methodology: To ingress need contrasting time for trial and their execution shows difference in terms of pace and memory requisites. Results: The result obtained shows the performance of the algorithms and models in the given conditions. It also reflects the best suited environment for the techniques. The work was accomplished by testing the algorithms for numerous sequences of input video. The identification rate analysis gives the identification rate of foreground pixels for various colors divergent from the background model.
Conclusions: The objective of computer vision is to imitate human vision utilizing advanced digital images through three principle handling parts that are executed consistently, i.e., acquisition of pictures, picture processing, and picture investigation and comprehension. It has gained a lot of attention from researchers in an enormous field. The basic purpose of this study was to prospecting tracking and computer-aided object detection techniques. For this purpose, a number of methods were observed, studied critically, and used. Originality: In the study, an improved frame differencing method for tracking an object is briefly reviewed, and an improved version of the algorithm was also implemented using MATLAB. The proposed algorithm was tested over distinct video sequences, and it was observed that the objects in motion were identified with minimal error rate when compared to a traditional frame differencing method. Limitations: One of the major challenges in the process of tracking objects in motion is to design algorithms or techniques for tracking the objects present in disrupted or random videos, like videos attained from broadcast news networks or home videos. These videos contain noise, and some of them may be unstructured, compressed, and denominationally having edited pieces obtained in different ways by moving cameras. |