Recognizing human actions in dark videos is a useful yet challenging visual task in reality. Existing augmentation-based methods separate action recognition and dark enhancement in a two-stage pipeline, which leads to inconsistently learning of temporal representation for action recognition. To address this issue, we propose a novel end-to-end framework termed Dark Temporal Consistency Model (DTCM), which is able to jointly optimize dark enhancement and action recognition, and force the temporal consistency to guide downstream dark feature learning. Specifically, DTCM cascades the action classification head with the dark augmentation network to perform dark video action recognition in a one-stage pipeline. Our explored spatio-temporal consistency loss, which utilizes the RGB-Difference of dark video frames to encourage temporal coherence of the enhanced video frames, is effective for boosting spatio-temporal representation learning. Extensive experiments demonstrated that our DTCM has remarkable performance: 1) Competitive accuracy, which outperforms the state-of-the-arts on the ARID dataset by 2.32% and the UAVHuman-Fisheye dataset by 4.19% in accuracy, respectively; 2) High efficiency, which surpasses the current most advanced method (Chen et al., 2021) with only 6.4% GFLOPs and 71.3% number of parameters; 3) Strong generalization, which can be used in various action recognition methods (e.g., TSM, I3D, 3D-ResNext-101, Video-Swin) to promote their performance significantly.

Action visual tempo characterizes the dynamics and the temporal scale of an action, which is helpful to distinguish human actions that share high similarities in visual dynamics and appearance. Previous methods capture the visual tempo either by sampling raw videos with multiple rates, which require a costly multi-layer network to handle each rate, or by hierarchically sampling backbone features, which rely heavily on high-level features that miss fine-grained temporal dynamics. In this work, we propose a Temporal Correlation Module (TCM), which can be easily embedded into the current action recognition backbones in a plug-in-and-play manner, to extract action visual tempo from low-level backbone features at single-layer remarkably. Specifically, our TCM contains two main components: a Multi-scale Temporal Dynamics Module (MTDM) and a Temporal Attention Module (TAM). MTDM applies a correlation operation to learn pixel-wise fine-grained temporal dynamics for both fast-tempo and slow-tempo. TAM adaptively emphasizes expressive features and suppresses inessential ones via analyzing the global information across various tempos. Extensive experiments conducted on several action recognition benchmarks, e.g. Something-Something V1&V2, Kinetics-400, UCF-101, and HMDB-51, have demonstrated that the proposed TCM is effective to promote the performance of the existing video-based action recognition models for a large margin. The source code is publicly released at https://github.com/zphyix/TCM.

In recent years, the method of obtaining ground object properties by processing and analyzing a 3D point cloud is prevalent. In this paper, a tree height estimation algorithm based on a 3D point cloud is proposed. This algorithm can calculate the height of trees in sparse scenes in batches, and it is also efficient. The key of the algorithm is to obtain the accurate point cloud cluster of the monomer tree. We achieve this by using a refined Density-based spatial clustering of applications with noise (DBSCAN). Then we estimate the ground height and treetop height, the height of the tree is their difference. In order to reduce the number of point clouds and maintain the overall structure, Voxel Grid filtering is also used. We apply this method to the 3D obtained point cloud, and good results prove our excellent work. Our code and point cloud data are available at https://github.com/yzfly/SimpleTreeHeight.