Intelligent animal detection system using sparse multi discriminative-neural network (SMD-NN) to mitigate animal-vehicle collision.

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Abstract

Animal-Vehicle Collision (AVC) is a predominant problem in both urban and rural roads and highways. Detecting animals on the road is challenging due to factors like the fast movement of both animals and vehicles, highly cluttered environmental settings, noisy images, and occluded animals. Deep learning has been widely used for animal applications. However, they require large training data; henceforth, the dimensionality increases, leading to a complex model. In this paper, we present an animal detection system for mitigating AVC. The proposed system integrates sparse representation and deep features optimized with FixResNeXt. The deep features extracted from candidate parts of the animals are represented in a sparse form using a feature-efficient learning algorithm called Sparse Network of Winnows (SNoW). The experimental results prove that the proposed system is invariant to the viewpoint, partial occlusion, and illumination. On the benchmark datasets, the proposed system has achieved an average accuracy of 98.5%.