Target Tracking by a Single Camera Based on Range-Window Algorithm and Pattern Matching

Miyahara, Shunji; Sielagoski, Jerry; Koulinitch, Anatoli; Ibrahim, Faroog

doi:10.4271/2006-01-0140

Cited by 11 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Authors in [10] discussed the application of Radars in navigation for autonomous vehicles, using an extended Kalman filter to fuse the radar and vehicle control signals for estimating vehicle position. In [11] authors proposed a correlation based pattern matching algorithm in addition to a range-window to detect and track objects in front of a vehicle. In [12] Ji et al proposed an attention selection system based on Radar detections to find candidate targets and employ a classification network to classify those objects.…”

Section: Related Workmentioning

confidence: 99%

RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles

Nabati

2019

2019 IEEE International Conference on Image Processing (ICIP)

153

View full text Add to dashboard Cite

Region proposal algorithms play an important role in most state-of-the-art two-stage object detection networks by hypothesizing object locations in the image. Nonetheless, region proposal algorithms are known to be the bottleneck in most two-stage object detection networks, increasing the processing time for each image and resulting in slow networks not suitable for real-time applications such as autonomous driving vehicles. In this paper we introduce RRPN, a Radarbased real-time region proposal algorithm for object detection in autonomous driving vehicles. RRPN generates object proposals by mapping Radar detections to the image coordinate system and generating pre-defined anchor boxes for each mapped Radar detection point. These anchor boxes are then transformed and scaled based on the object's distance from the vehicle, to provide more accurate proposals for the detected objects. We evaluate our method on the newly released NuScenes dataset [1] using the Fast R-CNN object detection network [2]. Compared to the Selective Search object proposal algorithm [3], our model operates more than 100× faster while at the same time achieves higher detection precision and recall. Code has been made publicly available at https://github.com/mrnabati/RRPN.

show abstract

Section: Related Workmentioning

confidence: 99%