The estimation of 3D human poses from time-synchronized, calibrated multi-view video usually consists of two steps: (1) a 2D detector to locate the 2D coordinate point position of the joint via heatmaps for each frame and (2) a post-processing method such as the recursive pictorial structure model or robust triangulation to obtain 3D coordinate points. However, most existing methods are based on a single frame only. They do not take advantage of the temporal characteristics of the video sequence itself, and must rely on post-processing algorithms. They are also susceptible to human self-occlusion, and the generated sequences suffer from jitter. Therefore, we propose a network model incorporating spatial and temporal features. Using a coarse-to-fine approach, the proposed heatmap temporal network (HTN) generates temporal heatmap information, with an occlusion heatmap filter used to filter low-quality heatmaps before they are sent to the HTN. The heatmap fusion and the triangulation weights are dynamically adjusted, and intermediate supervision is employed to enable better integration of temporal and spatial information. Our network is also end-to-end differentiable. This overcomes the long-standing problem of skeleton jitter being generated and ensures that the sequence is smooth and stable.

In this paper, we present our solution for tiger detection in the 2019 Computer Vision for Wildlife Conservation Challenge (CVWC2019). We introduce an efficient deep tiger detector, which consists of the convnet channel adaptation method and an improved tiger detection method based on You Only Look Once version 3 (YOLOv3). Considering the limited memory and computing power of tiny embedded devices, we have used EfficientNet-B0 and Darknet-53 as backbone networks for detection and adapted them to balance their depth and width inspired by the channel pruning method and knowledge distillation method. Our results show that after an architecture adjustment of Darknet-53, the floating-point computation decreases by 93%, its model size decreases by 97%, and its accuracy only decreases by 1%; after an architecture adjustment of EfficientNet-B0, the floating-point computation decreases by 66%, its model size decreases by 70% with its accuracy only decreased by 1%. We also compare GIoU loss and MSE loss in the training stage. The GIoU loss has the advantage that it increases the average AP for IoU from 0.5 to 0.95 without affecting training speed and the interface speed, so it is experimentally reasonable for tiger detection in the wild. This proposed method outperforms previous Amur tiger detection methods presented at CVWC2019.