Thesis subject

Msc thesis subject: Monitor Activity of Wild Animals using Thermopile Infrared Sensors and Cameras

Wild animal monitoring is important for preservation of various endangered species. Wildlife monitoring system has progressed with the development of Global Positioning System (GPS) and wireless sensor network (WSN). Meanwhile, there are still many challenges. One of the challenges is the lifetime of devices. Most devices rely on battery for monitoring animals in wild area. Existing works for animal monitoring use cameras to shoot all the pictures of animals and send them back to the server for further recognition. These approaches consume much energy on capturing images/video. On the one hand, it is difficulty or even impossible to change battery frequently in a wild area. On the other hand, if we increase the size of battery, the size and cost of the devices will increase. Therefore, we need a solution to minimize the power consumption in animal monitoring system.

The student should build a two-stage monitoring system using thermopile sensors and cameras. Compared with existing camera based monitoring solutions, the aim of this system is to increase the lifetime.

The operation of the system includes two steps.

Step 1: IR sensors are used to monitor animals. Compared with cameras, IR sensors have much lower power consumption, therefore we use it for full-time monitoring. After capturing the IR image of animals, the system recognize whether it is the target animal.

Step 2: If the target animal is in the captured IR image, then the camera is switched on. The camera is used to capture the image/video of the animal. Then, image recognition is used again to check if it is the target animal. Finally, the system sends the camera image back to the server.

    To implement the system, the student must finish the following work:

    1. Setup the system, including IR sensors, surveillance cameras, and wireless communication network between end node and server.
    2. Make animal recognition based on IR sensing images and machine learning models.
    3. Make animal recognition based on Camera images and machine learning models.


    • Munkhjargal Gochoo ; Tan-Hsu Tan ; Tsedevdorj Batjargal ; Oleg Seredin ; Shih-Chia Huang. Device-Free Non-Privacy Invasive Indoor Human Posture Recognition Using Low-Resolution Infrared Sensor-Based Wireless Sensor Networks and DCNN. 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 7-10 Oct. 2018.
    • Munkhjargal Gochoo ; Tan-Hsu Tan ; Shih-Chia Huang ; Tsedevdorj Batjargal ; Jun-Wei Hsieh. Novel IoT-Based Privacy-Preserving Yoga Posture Recognition System Using Low-Resolution Infrared Sensors and Deep Learning. IEEE Internet of Things Journal ( Volume: 6 , Issue: 4 , Aug. 2019 )
    • Michael A. Tabak Mohammad S. Norouzzadeh David W. Wolfson. Machine learning to classify animal species in camera trap images: Applications in ecology. Methods in Ecology and Evolution, 2019.

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