Behavioural analysis based on video recording is becoming increasingly popular within research fields such as; ecology, medicine, ecotoxicology and toxicology. However, the programs available to analyse the data, which are free of cost, user‐friendly, versatile, robust, fast and provide reliable statistics for different organisms (invertebrates, vertebrates and mammals) are significantly limited. We present an automated open‐source executable software (ToxTrac) for image‐based tracking that can simultaneously handle several organisms monitored in a laboratory environment. We compare the performance of ToxTrac with current accessible programs on the web. The main advantages of ToxTrac are as follows: (i) no specific knowledge of the geometry of the tracked bodies is needed; (ii) processing speed, ToxTrac can operate at a rate >25 frames per second in HD videos using modern computers; (iii) simultaneous tracking of multiple organisms in multiple arenas; (iv) integrated distortion correction and camera calibration; (v) robust against false positives; (vi) preservation of individual identification; (vii) useful statistics and heat maps in real scale are exported in image, text and excel formats. ToxTrac can be used for high speed tracking of insects, fish, rodents or other species, and provides useful locomotor information in animal behavior experiments. Download ToxTrac here: https://toxtrac.sourceforge.io (Current version v2.61).

No 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.

No abstract