We present a small system for counting and classifying bikers and pedestrians on nature trails. The system consists of a low-cost capture system based on the Raspberry Pi 2 and an embedded thermal camera. Besides the benefit of enabling both day- and night-time surveillance, thermal imaging also helps address the privacy concerns that usually plague surveillance systems. The camera is very low resolution, but it is able to provide sufficient information for a detector to locate and discriminate between bikers and pedestrians. The detector uses a typical sliding window-based approach and performs classification based on HoG features. Detections are collected in tracks from which a final decision is made on whether a biker or pedestrian has passed through the camera’s view. The system is trained and evaluated on a challenging new dataset with more than 25 h of thermal imagery. Data was captured from varying view points and from multiple geographical locations.The purpose is to show the feasibility of using a collection of classic computer vision methods and low-cost components for a real-time thermal surveillance system that is capable of classifying the different actors that make use of nature trails.
Original languageDanish
Title of host publicationThe 5th International Symposium on Sensor Science
Number of pages1
PublisherMultidisciplinary Digital Publishing Institute
Publication date13 Feb 2018
Publication statusPublished - 13 Feb 2018
Event5th International Symposium on Sensor Science - Barcelona, Spain
Duration: 27 Sep 201729 Sep 2017


Conference5th International Symposium on Sensor Science

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