Light-Weight Mixed Stage Partial Network for Surveillance Object Detection with Background Data Augmentation

Chen Ping-Yang, Jun-Wei Hsieh, Munkhjargal Gochoo, Yong-Sheng Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

State-of-the-art (SoTA) models have improved object detection accuracy with a large margin via convolutional neural networks, however still with an inferior performance for small objects. Moreover, these models are trained mainly based on the COCO dataset, and its backgrounds are more complicated than road environments, and thus degrade the accuracy of small road object detection. Compared with the COCO dataset, the background of a surveillance video is relatively stable and can be used to enhance the accuracy of road object detection. This paper designs a computationally efficient mixed stage partial (MSP) network to detect road objects. Another novelty of this paper is to propose a mixed background data augmentation method to enhance the detection accuracy without adding new labelling efforts. During inference, only the input image is used to detect road objects without further using any subtraction information. Extensive experiments on KITTI and UA-DETRAC benchmarks show the proposed method achieves the SoTA results for highly-accurate and efficient road object detection.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings
PublisherIEEE Computer Society
Pages3333-3337
Number of pages5
ISBN (Electronic)9781665441155
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Image Processing, ICIP 2021 - Anchorage, United States
Duration: 19 Sep 202122 Sep 2021

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2021-September
ISSN (Print)1522-4880

Conference

Conference2021 IEEE International Conference on Image Processing, ICIP 2021
Country/TerritoryUnited States
CityAnchorage
Period19/09/2122/09/21

Keywords

  • Background subtraction
  • Mixing Background Augmentation (MBA)
  • MSPNet
  • Road object detection

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