Real-time Detection, Tracking, and Classification of Moving and Stationary Objects using Multiple Fisheye Images
- Resource Type
- Conference
- Authors
- Baek, Iljoo; Davies, Albert; Yan, Geng; Rajkumar, Ragunathan Raj
- Source
- 2018 IEEE Intelligent Vehicles Symposium (IV) Intelligent Vehicles Symposium (IV), 2018 IEEE. :447-452 Jun, 2018
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Feature extraction
Convolution
Automobiles
Streaming media
Autonomous vehicles
Tracking
Cameras
- Language
The ability to detect pedestrians and other moving objects is crucial for an autonomous vehicle. This must be done in real-time with minimum system overhead. This paper discusses the implementationof a surround view system to identify moving as well as static objects that are close to the ego vehicle. The algorithm works on 4 views captured by fisheye cameras which are merged into a single frame. The moving object detection and tracking solution uses minimal system overhead to isolate regions of interest (ROIs) containing moving objects. These ROIs are then analyzed using a deep neural network (DNN) to categorize the moving object. With deployment and testing on a real car in urban environments, we have demonstrated the practical feasibility of the solution. 1 1 The video demos of our algorithm have been uploaded to Youtube: https://youtu.be/vpoCfC724iA, https://youtu.be/2X4aqH2bMBs