Fall Detection Using Modular Neural Networks With Back-Projected Optical Flow

Huang, Chieh-Ling; Chen, E-Liang; Chung, Pau‐Choo

doi:10.1142/s1016237207000525

Cited by 1 publication

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“…Most methods in the literature for fall detection use optical flow direction or magnitude etc. [18,23,24]. Here, they are not effective due to the very low fps.…”

Section: Anomaly Detectionmentioning

confidence: 99%

Ground-truthing Large Human Behavior Monitoring Datasets

Qasim

Fisher

Bhatti

2021

2020 25th International Conference on Pattern Recognition (ICPR)

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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 finetuned 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%.

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“…Most methods in the literature for fall detection use optical flow direction or magnitude etc. [18,23,24]. Here, they are not effective due to the very low fps.…”

Section: Anomaly Detectionmentioning

confidence: 99%