{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:56:59Z","timestamp":1775145419413,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T00:00:00Z","timestamp":1753833600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Italian Ministry of Research (MUR)","award":["CUPE83C22004640001"],"award-info":[{"award-number":["CUPE83C22004640001"]}]},{"name":"European Union\u2014Next Generation EU under the Italian National Recovery and Resilience Plan (NRRP)","award":["CUPE83C22004640001"],"award-info":[{"award-number":["CUPE83C22004640001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Real-time wildlife monitoring on edge devices poses significant challenges due to limited power, constrained bandwidth, and unreliable connectivity, especially in remote natural habitats. Conventional object detection systems often transmit redundant data of the same animals detected across multiple consecutive frames as a part of a single event, resulting in increased power consumption and inefficient bandwidth usage. Furthermore, maintaining consistent animal identities in the wild is difficult due to occlusions, variable lighting, and complex environments. In this study, we propose a lightweight hybrid tracking framework built on the YOLOv8m deep neural network, combining motion-based Kalman filtering with Local Binary Pattern (LBP) similarity for appearance-based re-identification using texture and color features. To handle ambiguous cases, we further incorporate Hue-Saturation-Value (HSV) color space similarity. This approach enhances identity consistency across frames while reducing redundant transmissions. The framework is optimized for real-time deployment on edge platforms such as NVIDIA Jetson Orin Nano and Raspberry Pi 5. We evaluate our method against state-of-the-art trackers using event-based metrics such as MOTA, HOTA, and IDF1, with a focus on detected animals occlusion handling, trajectory analysis, and counting during both day and night. Our approach significantly enhances tracking robustness, reduces ID switches, and provides more accurate detection and counting compared to existing methods. When transmitting time-series data and detected frames, it achieves up to 99.87% bandwidth savings and 99.67% power reduction, making it highly suitable for edge-based wildlife monitoring in resource-constrained environments.<\/jats:p>","DOI":"10.3390\/computers14080307","type":"journal-article","created":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:00:41Z","timestamp":1753884041000},"page":"307","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Smart Wildlife Monitoring: Real-Time Hybrid Tracking Using Kalman Filter and Local Binary Similarity Matching on Edge Network"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7252-5373","authenticated-orcid":false,"given":"Md. Auhidur","family":"Rahman","sequence":"first","affiliation":[{"name":"Department of Information Engineering, University of Pisa, Via G. Caruso 16, 56122 Pisa, Italy"},{"name":"Software Engineering Program, Institute of Information Technology, Noakhali Science and Technology University, Noakhali 3814, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1408-7528","authenticated-orcid":false,"given":"Stefano","family":"Giordano","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Pisa, Via G. Caruso 16, 56122 Pisa, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1706-4994","authenticated-orcid":false,"given":"Michele","family":"Pagano","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Pisa, Via G. Caruso 16, 56122 Pisa, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1089\/zeb.2011.0720","article-title":"Behavioral measure of frequency detection and discrimination in the zebrafish, Danio rerio","volume":"9","author":"Cervi","year":"2012","journal-title":"Zebrafish"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ferrante, G.S., Vasconcelos Nakamura, L.H., Sampaio, S., Filho, G.P.R., and Meneguette, R.I. (2024). Evaluating YOLO architectures for detecting road killed endangered Brazilian animals. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-52054-y"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e568","DOI":"10.1111\/csp2.568","article-title":"Methods for wildlife monitoring in tropical forests: Comparing human observations, camera traps, and passive acoustic sensors","volume":"3","author":"Zwerts","year":"2021","journal-title":"Conserv. Sci. 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