252 research outputs found
Repulsion Loss: Detecting Pedestrians in a Crowd
Detecting individual pedestrians in a crowd remains a challenging problem
since the pedestrians often gather together and occlude each other in
real-world scenarios. In this paper, we first explore how a state-of-the-art
pedestrian detector is harmed by crowd occlusion via experimentation, providing
insights into the crowd occlusion problem. Then, we propose a novel bounding
box regression loss specifically designed for crowd scenes, termed repulsion
loss. This loss is driven by two motivations: the attraction by target, and the
repulsion by other surrounding objects. The repulsion term prevents the
proposal from shifting to surrounding objects thus leading to more crowd-robust
localization. Our detector trained by repulsion loss outperforms all the
state-of-the-art methods with a significant improvement in occlusion cases.Comment: Accepted to IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 201
Forward Vehicle Collision Warning Based on Quick Camera Calibration
Forward Vehicle Collision Warning (FCW) is one of the most important
functions for autonomous vehicles. In this procedure, vehicle detection and
distance measurement are core components, requiring accurate localization and
estimation. In this paper, we propose a simple but efficient forward vehicle
collision warning framework by aggregating monocular distance measurement and
precise vehicle detection. In order to obtain forward vehicle distance, a quick
camera calibration method which only needs three physical points to calibrate
related camera parameters is utilized. As for the forward vehicle detection, a
multi-scale detection algorithm that regards the result of calibration as
distance priori is proposed to improve the precision. Intensive experiments are
conducted in our established real scene dataset and the results have
demonstrated the effectiveness of the proposed framework
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