Assessing machine learning classifiers for the detection of animals' behavior using depth-based tracking

[EN] There is growing interest in the automatic detection of animals' behaviors and body postures within the field of Animal Computer Interaction, and the benefits this could bring to animal welfare, enabling remote communication, welfare assessment, detection of behavioral patterns, interactive and adaptive systems, etc. Most of the works on animals' behavior recognition rely on wearable sensors to gather information about the animals' postures and movements, which are then processed using machine learning techniques. However, non-wearable mechanisms such as depth-based tracking could also make use of machine learning techniques and classifiers for the automatic detection of animals' behavior. These systems also offer the advantage of working in set-ups in which wearable devices would be difficult to use. This paper presents a depth-based tracking system for the automatic detection of animals' postures and body parts, as well as an exhaustive evaluation on the performance of several classification algorithms based on both a supervised and a knowledge-based approach. The evaluation of the depth -based tracking system and the different classifiers shows that the system proposed is promising for advancing the research on animals' behavior recognition within and outside the field of Animal Computer Interaction. (C) 2017 Elsevier Ltd. All rights reserved.This work is funded by the European Development Regional Fund (EDRF-FEDER) and supported by Spanish MINECO with Project TIN2014-60077-R. It also received support from a postdoctoral fellowship within the VALi+d Program of the Conselleria d'Educacio, Cultura I Esport (Generalitat Valenciana) awarded to Alejandro Catala (APOSTD/2013/013). The work of Patricia Pons is supported by a national grant from the Spanish MECD (FPU13/03831). Special thanks to our cat participants and their owners, and many thanks to our feline caretakers and therapists, Olga, Asier and Julia, for their valuable collaboration and their dedication to animal wellbeing.Pons Tomás, P.; Jaén Martínez, FJ.; Catalá Bolós, A. (2017). Assessing machine learning classifiers for the detection of animals' behavior using depth-based tracking. Expert Systems with Applications. 86:235-246. https://doi.org/10.1016/j.eswa.2017.05.063S2352468

Monitoring system for detecting the motility and position of laboratory animals after anesthesia

Tato Diplomová práce, která nese název „Monitorovací systém pro zjištění motility a polohy laboratorních zvířat po anestézii“ se zaměřuje na návrh a realizaci bezkontaktní detekce polohy laboratorního potkana nebo myši ve výběhu s průhledným krytem. Semestrální práce si klade za cíl nalézt vhodné metody realizaci bezkontaktní detekce polohy laboratorního potkana nebo myši a automaticky určit a zobrazit průměrnou rychlost nebo jiné charakteristiky pohybu. Zadání vzešlo z potřeb monitoringu zvířat po kurativním zásahu a také jako potřebná utilita pro budoucí „stínování“ pohyb zvířete (automatické cílení na jizvu na zádech zvířete). Potkan, který je umístěný uvnitř našeho výběhu je buď standardně pohyblivý nebo je omámen po anestezií. V této práci se zabývám nejprve rešerší automatických monitorovacích systémů pro detekci polohy zvířat ve výběhu. Pak v praktické části jsou testovány tři typy kamer pro obrazovou detekci polohy potkana a je navrhnut skript pro automatickou detekci a analýzu pohybu potkana. Systém funguje jako kamerové oko které v reálnem času v svém zorném poli schopno najit plochu černého boxu následně omezit plochu detekci o velikosti teto krabice a nasledne v omezenem prostoru automaticky detekuje težište a vzpočitava cestu kterou udelava ten bod , jako stitistiku uklada cestu za deasat sekund a z toho vzpočitava přumernou rzchlost potkanu za tu dobu .A hodnocenim získanou rzchlosti s průměrem vypočtenzm s testev na 10 myšich - hlasi na obrazovce stav myši za predešlych desat sekund.Vytvořeny software detekuje bilou myš nebo potkana v černém hovnem boxu , bez doplňkového označeni zvířeti pomoci markéru pro žádné stresovaní zvířeti . Potkan, který je umístěný uvnitř našeho výběhu je buď standardně pohyblivý nebo je omámen po anestezií. V této práci se zabývám nejprve rešerší automatických monitorovacích systémů pro detekci polohy zvířat ve výběhu. Pak v praktické části jsou testovány tři typy kamer pro obrazovou detekci polohy potkana a je navrhnut skript pro automatickou detekci a analýzu pohybu potkana. A vytvořen software na detekciThis diploma thesis, entitled "Monitoring System for Determination of Motility and Position of Laboratory Animals After Anesthesia", focuses on the design and implementation of contactless detection of the position of a rat or mouse in an enclosure with a transparent cover. The aim of the semester work is to find suitable methods of realization of contactless detection of rat or mouse position and to automatically determine and display average speed or other movement characteristics. The assignment arose from the needs of animal monitoring after curative intervention and also as a necessary utility for future "shading" animal movement (automatic targeting of the scar on the animal's back). The rat, which is located inside our enclosure, is either moving as standard or is dazed after anesthesia. In this work I deal first with search of automatic monitoring systems for detection of animals in the enclosure. Then in the practical part are tested three types of cameras for visual detection of rat position and a script for automatic detection and analysis of rat movement is designed. The system works like a camera eye which in real time is able to find the area of a black box in its field of view and then limit the detection area to the size of this box and then automatically detects the center of gravity and counts. and evaluates the obtained speed with an average calculated with a test of 10 mice - voices on the screen the mouse status in the previous ten seconds. for no stressed animal The rat that is located inside our enclosure is either standard or movable after anesthesia. In this work I first deal with searches of automatic monitoring systems for detecting the position of animals in the enclosure. Then, in the practical part, I test three types of cameras for image detection of rat position. Evaluation software for motion analysis will largely be solved in the follow-up diploma thesis.Project made like monitoring and detecting software based on OpenCV.