Abstract
Video analysis of animal behaviour is widely used in fields such as ecology, ecotoxicology, and evolutionary research. However, when tracking multiple animals, occlusion and crossing are problematic, especially when the identity of each individual needs to be preserved. We present a new algorithm, ToxId, which preserves the identity of multiple animals by linking trajectory segments using their intensity histogram and Hu-moments. We verify the performance and accuracy of our algorithm using video sequences with different animals and experimental conditions. The results show that our algorithm achieves state-of-the-art accuracy using an efficient approach without the need of learning processes, complex feature maps or knowledge of the animal shape. ToxId is also computationally efficient, has low memory requirements, and operates without accessing future or past frames.
Highlights
Animal behaviour is important in many research fields such as ecology, medicine, neurology, ecotoxicology or evolutionary research[1]
To improve detection and tracking, some techniques use a specific model of the animal body based on the head shape[10,11], the body geometry[12,13,14] or the symmetry axis[15]
Some authors discuss the use of features such as face properties[16] or bilateral symmetry[17]
Summary
Animal behaviour is important in many research fields such as ecology, medicine, neurology, ecotoxicology or evolutionary research[1]. While several methods provide a reliable tool for tracking one single individual[4,5], preserving the identity of multiple individuals after an occlusion remains a challenging problem[1], see Fig. 1 for an example The complexity of this problem is illustrated in Pérez-Escudero et al.[6], in a scenario where they solved correctly 99% of all crossings, but when considering error propagation only 11% of the animals were correctly identified after 2 minutes of tracking. Other approaches to reduce occlusion problems rely on pattern recognition, matching specific texture maps[6] or using convolutional neural networks[18] to identify the animals These techniques are computationally and memory expensive and require access to past and future frames (offline tracking). The software, the user manual and the documentation are available at https://toxtrac.sourceforge.io
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