IterDet: Iterative Scheme for Object Detection in Crowded Environments

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)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Object Detection CrowdHuman (full body) IterDet (Faster RCNN, ResNet50, 2 iterations) AP 88.08 # 3
mMR 49.44 # 4
Object Detection CrowdHuman (full body) IterDet (Faster RCNN, ResNet50, 1 iteration) AP 84.43 # 8
mMR 49.12 # 3
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


METHOD TYPE
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