Computer vision is an interesting tool for animal behavior monitoring, mainly because it limits animal handling and it can be used to record various traits using only one sensor. From previous studies, this technic has shown to be suitable for various species and behavior. However it remains challenging to collect individual information, i.e. not only to detect animals and behavior on the video frames, but also to identify them. Animal identification is a prerequisite to gather individual information in order to characterize individuals and compare them. A common solution to this problem, known as multiple objects tracking, consists in detecting the animals on each video frame, and then associate detections to a unique animal ID. Association of detections between two consecutive frames are generally made to maintain coherence of the detection locations and appearances. To extract appearance information, a common solution is to use a convolutional neural network (CNN), trained on a large dataset before running the tracking algorithm. For farmed animals, designing such network is challenging as far as large training dataset are still lacking. In this article, we proposed an innovative solution, where the CNN used to extract appearance information is parameterized using offline unsupervised training. The algorithm, named Wizard, was evaluated for the purpose of goats monitoring in outdoor conditions. 17 annotated videos were used, for a total of 4H30, with various number of animals on the video (from 3 to 8) and different level of color differences between animals. First, the ability of the algorithm to track the detected animals was evaluated. When animals were detected, the algorithm found the correct animal ID in 94.82% of the frames. When tracking and detection were evaluated together, we found that Wizard found the correct animal ID in 86.18% of the video length. In situations where the animal detection rate could be high, Wizard seems to be a suitable solution for individual behavior analysis experiments based on computer vision.