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Ground-truthing Large Human Behavior Monitoring Datasets

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationProceedings of the 25th International Conference on Pattern Recognition (ICPR2020)
Number of pages22
Publication statusAccepted/In press - 11 Oct 2020
Event25th International Conference on Pattern Recognition 2020 - Milan, Italy
Duration: 10 Jan 202115 Jan 2021
https://www.micc.unifi.it/icpr2020/

Conference

Conference25th International Conference on Pattern Recognition 2020
Abbreviated titleICPR 2020
CountryItaly
CityMilan
Period10/01/2115/01/21
Internet address

Abstract

We present a groundtruthing approach which is applicable to large video datasets collected for studying people’s behavior, and which are recorded at a low frame per second (fps) rate. Groundtruthing a large dataset manually is a time consuming task and is prone to errors. The proposed approach is semi-automated (using a combination of deepnet and traditional image analysis) to minimize human labeler’s interaction with the video frames. The framework employs mask-rcnn as a people counter followed by human assisted semi-automated tests to correct the wrong labels. Subsequently, a bounding box extraction algorithm is used which is fully automated for frames with a single person and semi-automated for frames with two or more people. We also propose a methodology for anomaly detection i.e., collapse on table or floor. Behavior recognition is performed by using a fine-tuned alexnet convolutional neural network. The people detection and behavior analysis components of the framework are primarily designed to help reduce human labor in ground-truthing so that minimal human involvement is required. They are not meant to be employed as fully automated state-of-the-art systems. The proposed approach is validated on a new dataset presented in this paper, containing human activity in an indoor office environment and recorded at 1 fps as well as an indoor video sequence recorded at 15 fps. Experimental results show a significant reduction in human labor involved in the process of ground-truthing i.e., the number of potential clicks for office dataset was reduced by 99.2% and for the additional test video by 99.7%.

Event

25th International Conference on Pattern Recognition 2020

10/01/2115/01/21

Milan, Italy

Event: Conference

ID: 172855117