Vehicle Detection and Tracking using YOLO and DeepSORT
Authors:
- 3 April 2021
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 23-29
Abstract
Every year, the number of vehicles on the road will be increasing. as claimed by a road transport department (JPJ) data in Malaysia, there were around 31.2 million units of motor vehicles recorded in Malaysia as of December 31, 2019. While as, from the mid-2017, there were around 28.18 million units of motor vehicles recorded in Malaysia. Consequently, accurate and fast detection of vehicles on the road is needed by using the volume of vehicles as valuable data for detecting traffic congestion which then benefits for traffic management. Using the implemented deep learning for vehicle detection, this paper project is using TensorFlow which is platform for machine learning and you only look once (yolo) which is object detection algorithm for real-time vehicle detection. By combining this two and other dependencies with python as programming language, the suggested method in this paper determine the improvement of YOLOv4 latest algorithm compared to the previous model in vehicle detection system. This vehicle detection also uses DeepSORT algorithm to help counting the number of vehicles pass in the video effectively. From this paper, the best model between YOLO model is Yolov4 which had achieved state-of-the-art results with 82.08% AP50 using the custom dataset at a real time speed of around 14 FPS on GTX 1660ti.Keywords
Funding Information
- Ministry of Education for the FRGS (600-IRMI/FRGS 5/3/ (081/2019))
- Faculty of Electrical Engineering
- Universiti Teknologi MARA
This publication has 14 references indexed in Scilit:
- Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving AssistanceIEEE Transactions on Intelligent Transportation Systems, 2019
- A Deep Learning RCNN Approach for Vehicle Recognition in Traffic Surveillance System2019
- CNN Model Design of Gesture Recognition Based on Tensorflow Framework2019
- SINet: A Scale-Insensitive Convolutional Neural Network for Fast Vehicle DetectionIEEE Transactions on Intelligent Transportation Systems, 2018
- Automatic Moving Vehicle Detection and Classification Based on Artificial Neural Fuzzy Inference SystemWireless Personal Communications, 2018
- Kalman filter and Iterative-Hungarian Algorithm implementation for low complexity point tracking as part of fast multiple object tracking system2016
- You Only Look Once: Unified, Real-Time Object Detection2016
- Vehicle detection and attribute based search of vehicles in video surveillance system2015
- Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation2014
- Human detection and object tracking based on Histograms of Oriented Gradients2013