Progression-Guided Temporal Action Detection in Videos

We present a novel framework, Action Progression Network (APN), for temporal action detection (TAD) in videos. The framework locates actions in videos by detecting the action evolution process. To encode the action evolution, we quantify a complete action process into 101 ordered stages (0\%, 1\%, ..., 100\%), referred to as action progressions. We then train a neural network to recognize the action progressions. The framework detects action boundaries by detecting complete action processes in the videos, e.g., a video segment with detected action progressions closely follow the sequence 0\%, 1\%, ..., 100\%. The framework offers three major advantages: (1) Our neural networks are trained end-to-end, contrasting conventional methods that optimize modules separately; (2) The APN is trained using action frames exclusively, enabling models to be trained on action classification datasets and robust to videos with temporal background styles differing from those in training; (3) Our framework effectively avoids detecting incomplete actions and excels in detecting long-lasting actions due to the fine-grained and explicit encoding of the temporal structure of actions. Leveraging these advantages, the APN achieves competitive performance and significantly surpasses its counterparts in detecting long-lasting actions. With an IoU threshold of 0.5, the APN achieves a mean Average Precision (mAP) of 58.3\% on the THUMOS14 dataset and 98.9\% mAP on the DFMAD70 dataset.

Video Pose Track with Graph-Guided Sparse Motion Estimation

In this paper, we propose a novel framework for multi-person pose estimation and tracking under occlusions and motion blurs. Specifically, the consistency in graph structures from consecutive frames is improved by concentrating on visible body joints and estimating the motion vectors of sparse key-points surrounding visible joints. The proposed framework involves three components: (i) A Sparse Key-point Flow Estimating Module (SKFEM) for sampling key-points from around body joints and estimating the motion vectors of key-points which contribute to the refinement of body joint locations and fine-tuning of pose estimators; (ii) A Hierarchical Graph Distance Minimizing Module (HGMM) for evaluating the visibility scores of nodes from hierarchical graphs with the visibility score of a node determining the number of samples around that node; and (iii) The combination of multiple historical frames for matching identities. Graph matching with HGMM facilitates more accurate tracking even under partial occlusions. The proposed approach not only achieves state-of-the-art performance on the PoseTrack dataset but also contributes to significant improvements in human-related anomaly detection. Besides a higher accuracy, the proposed SKFEM also shows a much higher efficiency than dense optical flow estimation.