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Occlusion modeling is critical for light field depth estimation, since occlusion destroys the photo-consistency assumption, which most depth estimation methods hold. Previous works always detect the occlusion points on the basis of Canny detector, which can leave some occlusion points out. Occlusion handling, especially for multi-occluder occlusion, is still challenging. In this paper, we propose a novel occlusion-aware depth estimation method, which can better solve the occlusion problem. We design two novel consistency costs based on the photo-consistency for depth estimation. According to the consistency costs, we analyze the influence of the occlusion and propose an occlusion detection technique based on depth consistency, which can detect the occlusion points more accurately. For the occlusion point, we adopt a new data cost to select the un-occluded views, which are used to determine the depth. Experimental results demonstrate that the proposed method is superior to the other compared algorithms, especially in multi-occluder occlusions. [ABSTRACT FROM AUTHOR]

Simple Summary: The imagery captured by cameras provides important information for wildlife research and conservation. Deep learning technology can assist ecologists in automatically identifying and processing imagery captured from camera traps, improving research capabilities and efficiency. Currently, many general deep learning architectures have been proposed but few have evaluated their applicability for use in real camera trap scenarios. Our study constructed the Northeast Tiger and Leopard National Park wildlife dataset (NTLNP dataset) for the first time and compared the real-world application performance of three currently mainstream object detection models. We hope this study provides a reference on the applicability of the AI technique in wild real-life scenarios and truly help ecologists to conduct wildlife conservation, management, and research more effectively. Camera traps are widely used in wildlife surveys and biodiversity monitoring. Depending on its triggering mechanism, a large number of images or videos are sometimes accumulated. Some literature has proposed the application of deep learning techniques to automatically identify wildlife in camera trap imagery, which can significantly reduce manual work and speed up analysis processes. However, there are few studies validating and comparing the applicability of different models for object detection in real field monitoring scenarios. In this study, we firstly constructed a wildlife image dataset of the Northeast Tiger and Leopard National Park (NTLNP dataset). Furthermore, we evaluated the recognition performance of three currently mainstream object detection architectures and compared the performance of training models on day and night data separately versus together. In this experiment, we selected YOLOv5 series models (anchor-based one-stage), Cascade R-CNN under feature extractor HRNet32 (anchor-based two-stage), and FCOS under feature extractors ResNet50 and ResNet101 (anchor-free one-stage). The experimental results showed that performance of the object detection models of the day-night joint training is satisfying. Specifically, the average result of our models was 0.98 mAP (mean average precision) in the animal image detection and 88% accuracy in the animal video classification. One-stage YOLOv5m achieved the best recognition accuracy. With the help of AI technology, ecologists can extract information from masses of imagery potentially quickly and efficiently, saving much time. [ABSTRACT FROM AUTHOR]