Detection Selection Algorithm: A Likelihood based Optimization Method to Perform Post Processing for Object Detection

• URL: http://arxiv.org/abs/2212.05706v2
• Date: Mon, 3 Apr 2023 16:40:22 GMT
• Title: Detection Selection Algorithm: A Likelihood based Optimization Method to Perform Post Processing for Object Detection
• Authors: Angzhi Fan, Benjamin Ticknor and Yali Amit
• Abstract summary: In object detection, post-processing methods like Non-maximum Suppression (NMS) are widely used. In order to find the exact number of objects and their labels in the image, we propose a post processing method called Detection Selection Algorithm (DSA) DSA greedily selects a subset of detected bounding boxes, together with full object reconstructions that give the interpretation of the whole image with highest likelihood.
• Score: 1.7188280334580197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In object detection, post-processing methods like Non-maximum Suppression (NMS) are widely used. NMS can substantially reduce the number of false positive detections but may still keep some detections with low objectness scores. In order to find the exact number of objects and their labels in the image, we propose a post processing method called Detection Selection Algorithm (DSA) which is used after NMS or related methods. DSA greedily selects a subset of detected bounding boxes, together with full object reconstructions that give the interpretation of the whole image with highest likelihood, taking into account object occlusions. The algorithm consists of four components. First, we add an occlusion branch to Faster R-CNN to obtain occlusion relationships between objects. Second, we develop a single reconstruction algorithm which can reconstruct the whole appearance of an object given its visible part, based on the optimization of latent variables of a trained generative network which we call the decoder. Third, we propose a whole reconstruction algorithm which generates the joint reconstruction of all objects in a hypothesized interpretation, taking into account occlusion ordering. Finally we propose a greedy algorithm that incrementally adds or removes detections from a list to maximize the likelihood of the corresponding interpretation. DSA with NMS or Soft-NMS can achieve better results than NMS or Soft-NMS themselves, as is illustrated in our experiments on synthetic images with mutiple 3d objects.

Related papers

• Visual Multi-Object Tracking with Re-Identification and Occlusion Handling using Labeled Random Finite Sets [10.618186767487993]
  This paper proposes an online visual multi-object tracking (MOT) algorithm that resolves object appearance-reappearance and occlusion. Our solution is based on the labeled random finite set (LRFS) filtering approach. We propose a fuzzy detection model that takes into consideration the overlapping areas between tracks and their sizes.
  arXiv  Detail & Related papers  (2024-07-11T21:15:21Z)
• DR.CPO: Diversified and Realistic 3D Augmentation via Iterative Construction, Random Placement, and HPR Occlusion [4.64982780843177]
  In autonomous driving, data augmentation is commonly used to improve 3D object detection. We develop a diversified and realistic augmentation method that can flexibly construct a whole-body object. DR.CPO can improve mAP performance by 2.08% when compared to the best 3D detection result known for KITTI dataset.
  arXiv  Detail & Related papers  (2023-03-20T07:42:48Z)
• A Tri-Layer Plugin to Improve Occluded Detection [100.99802831241583]
  We propose a simple '' module for the detection head of two-stage object detectors to improve the recall of partially occluded objects. The module predicts a tri-layer of segmentation masks for the target object, the occluder and the occludee, and by doing so is able to better predict the mask of the target object. We also establish a COCO evaluation dataset to measure the recall performance of partially occluded and separated objects.
  arXiv  Detail & Related papers  (2022-10-18T17:59:51Z)
• Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection [77.3530907443279]
  We propose a novel self-supervised framework to detect objects in degraded low resolution images. Our methods has achieved superior performance compared with existing methods when facing variant degradation situations.
  arXiv  Detail & Related papers  (2022-08-05T09:36:13Z)
• You Better Look Twice: a new perspective for designing accurate detectors with reduced computations [56.34005280792013]
  BLT-net is a new low-computation two-stage object detection architecture. It reduces computations by separating objects from background using a very lite first-stage. Resulting image proposals are then processed in the second-stage by a highly accurate model.
  arXiv  Detail & Related papers  (2021-07-21T12:39:51Z)
• GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection [25.313894069303718]
  We present and integrate GrooMeD-NMS -- a novel Grouped Mathematically Differentiable NMS for monocular 3D object detection. GrooMeD-NMS addresses the mismatch between training and inference pipelines. It achieves state-of-the-art monocular 3D object detection results on the KITTI benchmark dataset.
  arXiv  Detail & Related papers  (2021-03-31T16:29:50Z)
• Object Detection Made Simpler by Eliminating Heuristic NMS [70.93004137521946]
  We show a simple NMS-free, end-to-end object detection framework. We attain on par or even improved detection accuracy compared with the original one-stage detector.
  arXiv  Detail & Related papers  (2021-01-28T02:38:29Z)
• End-to-End Object Detection with Fully Convolutional Network [71.56728221604158]
  We introduce a Prediction-aware One-To-One (POTO) label assignment for classification to enable end-to-end detection. A simple 3D Max Filtering (3DMF) is proposed to utilize the multi-scale features and improve the discriminability of convolutions in the local region. Our end-to-end framework achieves competitive performance against many state-of-the-art detectors with NMS on COCO and CrowdHuman datasets.
  arXiv  Detail & Related papers  (2020-12-07T09:14:55Z)
• Quantum-soft QUBO Suppression for Accurate Object Detection [8.871042314510788]
  Non-maximum suppression (NMS) has been adopted by default for removing redundant object detections for decades. We propose Quantum-soft QUBO Suppression (QSQS) algorithm for fast and accurate detection by exploiting quantum computing advantages.
  arXiv  Detail & Related papers  (2020-07-28T05:12:51Z)
• siaNMS: Non-Maximum Suppression with Siamese Networks for Multi-Camera 3D Object Detection [65.03384167873564]
  A siamese network is integrated into the pipeline of a well-known 3D object detector approach. associations are exploited to enhance the 3D box regression of the object. The experimental evaluation on the nuScenes dataset shows that the proposed method outperforms traditional NMS approaches.
  arXiv  Detail & Related papers  (2020-02-19T15:32:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.