IterDet: Iterative Scheme for ObjectDetection in Crowded Environments

12 May 2020Danila RukhovichKonstantin SofiiukDanil GaleevOlga BarinovaAnton Konushin

Deep learning-based detectors usually produce a redundant set of object bounding boxes including many duplicate detections of the same object. These boxes are then filtered using non-maximum suppression (NMS) in order to select exactly one bounding box per object of interest... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Object Detection CrowdHuman (full body) IterDet (Faster RCNN, ResNet50, 2 iterations) AP 88.08 # 1
mMR 49.44 # 2
Object Detection CrowdHuman (full body) IterDet (Faster RCNN, ResNet50, 1 iteration) AP 84.43 # 6
mMR 49.12 # 1
Object Detection WiderPerson IterDet (Faster RCNN, ResNet50, 2 iterations) AP 91.95 # 1
mMR 40.78 # 2
Object Detection WiderPerson IterDet (Faster RCNN, ResNet50, 1 iteration) AP 89.49 # 3
mMR 40.35 # 1

Methods used in the Paper


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