Review and Classification of Trending Background Subtraction-Based Object Detection Techniques

2020 ◽  
Author(s):  
Bayar Azeez ◽  
Fattah Alizadeh
Keyword(s):  
Object Detection ◽  
Background Subtraction ◽  
Detection Techniques

scholarly journals A Set of Single YOLO Modalities to Detect Occluded Entities via Viewpoint Conversion

2021 ◽  
Vol 11 (13) ◽  
pp. 6016
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Cloud Data ◽  
Detection Techniques ◽  
Bounding Boxes ◽  
Partially Occluded ◽  
Rgb Image

For autonomous vehicles, it is critical to be aware of the driving environment to avoid collisions and drive safely. The recent evolution of convolutional neural networks has contributed significantly to accelerating the development of object detection techniques that enable autonomous vehicles to handle rapid changes in various driving environments. However, collisions in an autonomous driving environment can still occur due to undetected obstacles and various perception problems, particularly occlusion. Thus, we propose a robust object detection algorithm for environments in which objects are truncated or occluded by employing RGB image and light detection and ranging (LiDAR) bird’s eye view (BEV) representations. This structure combines independent detection results obtained in parallel through “you only look once” networks using an RGB image and a height map converted from the BEV representations of LiDAR’s point cloud data (PCD). The region proposal of an object is determined via non-maximum suppression, which suppresses the bounding boxes of adjacent regions. A performance evaluation of the proposed scheme was performed using the KITTI vision benchmark suite dataset. The results demonstrate the detection accuracy in the case of integration of PCD BEV representations is superior to when only an RGB camera is used. In addition, robustness is improved by significantly enhancing detection accuracy even when the target objects are partially occluded when viewed from the front, which demonstrates that the proposed algorithm outperforms the conventional RGB-based model.

Analysis Of Various Object Detection Techniques for Self-Driving Cars

2021 ◽  
Author(s):  
Yukta Lapsiya ◽  
Dhruvi Jain ◽  
Parshva Shah ◽  
Atul Kachare
Keyword(s):  
Object Detection ◽  
Detection Techniques ◽  
Self Driving Cars

scholarly journals Moving Object Detection and Segmentation using Background Subtraction by Kalman Filter

2017 ◽  
Vol 10 (19) ◽  
pp. 1-11 ◽  
Author(s):  
Archita Tah ◽  
Sudipta Roy ◽  
Prasenjit Das ◽  
Anirban Mitra ◽  
◽  
...  
Keyword(s):  
Kalman Filter ◽  
Object Detection ◽  
Background Subtraction ◽  
Moving Object Detection ◽  
Moving Object
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