Abstract
Deep learning-based detectors tend to produce duplicate detections of the same objects. After that, the detections are filtered via a non-maximum suppression algorithm (NMS) so that there remains only one bounding box per object. This simple greedy scheme is sufficient for isolated objects. However, it often fails in crowded environments since boxes for different objects should be preserved and duplicate detections should be suppressed at the same time. In this work, we propose to obtain predictions following iterative scheme called IterDet. At each iteration, a new subset of objects is detected. Detected boxes from all the previous iterations are considered at the current iteration to ensure that the same object would not be detected twice. This iterative scheme can be applied to both one-stage and two-stage deep learning-based detectors with minor modifications. Through extensive evaluation on 4 diverse datasets with two different baseline detectors, we prove our iterative scheme to achieve significant improvement over the baseline. On CrowdHuman and WiderPerson datasets, we obtain state-of-the-art results. The source code and the trained models are available at https://github.com/saic-vul/iterdet.
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Rukhovich, D., Sofiiuk, K., Galeev, D., Barinova, O., Konushin, A. (2021). IterDet: Iterative Scheme for Object Detection in Crowded Environments. In: Torsello, A., Rossi, L., Pelillo, M., Biggio, B., Robles-Kelly, A. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2021. Lecture Notes in Computer Science(), vol 12644. Springer, Cham. https://doi.org/10.1007/978-3-030-73973-7_33
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