Images of crowded scenes typically have been challenging for human-detection and pose-estimation algorithms. Top-down approaches suffer from reliance on non-maximum suppression (NMS) algorithms, which often remove valid detections, while bottom-up approaches inconsistently associate body parts of different people into the same detection. This disclosure presents techniques that combine elements of both top-down and bottom-up approaches, by leveraging the observation that head-boxes overlap less with each other as compared to body-boxes. NMS algorithms are applied to head-boxes instead of body-boxes. Head boxes are detected jointly, and are matched to the corresponding body-boxes. The techniques improve detection and pose estimation results for images of crowded scenes.
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Anonymous, "Multibox Human Detection for Images of Crowded Scenes", Technical Disclosure Commons, (June 10, 2020)