Authors:
Jalila Filali
1
;
2
;
Denis Laurendeau
1
;
2
and
Steeve Côté
3
;
4
Affiliations:
1
Department of Electrical and Computer Engineering, Faculty of Sciences and Engineering, Laval University, Quebec, Canada
;
2
Computer Vision and Systems Laboratory (CVSL), Laval University, Quebec, Canada
;
3
Department of Biology, Faculty of Sciences and Engineering, Laval University, Quebec, Canada
;
4
Caribou Ungava, Centre for Northern Studies (CEN), Laval University, Quebec, Canada
Keyword(s):
YOLOv5 Model, Video Object Detection, Video Stabilization, Environmental Information Extraction, Data Visualization.
Abstract:
With the rapid increase in the number of recorded videos, developing and exploring intelligent systems become more prominent to analyze video content. Within projects related to Sentinel North’s research program$^*$, our project involves how to analyze videos that are collected using camera collars installed on caribou (Rangifer tarandus) and black bears (Ursus americanus) living in northern Quebec. Our objective was to extract valuable environmental information such as weather, resources, and habitat where animals live. In this paper, we propose an environmental information extraction approach based on YOLOv5-Object detection in videos collected by camera collars installed on caribou and black bears in Northern Quebec. Our proposal consists, firstly, in filtering raw data and stabilizing videos to build a wildlife video dataset for deep learning training and evaluating object detection. Secondly, it focuses on solving the existing difficulties in detecting objects by adopting the Y
OLOv5 model to incorporate enriched features and detect objects of different sizes, and it further allows us to exploit and analyze object detection results to extract relevant information about weather, resources, and habitat of animals. Finally, it consists in visualizing object detection and statistical results by developing a GUI interface. The experimental results show that the YOLOv5m model was significantly better than the YOLOv5s model and can detect objects with different sizes. In addition, the obtained results show that our method can extract weather, habitat, and resource classes from stabilized videos, and then determine their percentage of appearance. Moreover, our proposed method can automatically provide statistics about environmental information for each stabilized video.
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