There is a growing demand for online fitness due to the impact of the epidemic. This paper presents a real-time online fitness system framework called AICoacher, which offers different online coaches. The framework constructs an extensible AI-based architecture that supports a variety of fitness movements. Firstly, key frames of motion are extracted automatically, and the feature vectors are calculated with the body pose points. Secondly, the state transition matrix can effectively identify fitness actions and capture their time-continuous characteristics. Finally, AICoacher can accurately provide the number of repetitions and correction tips of fitness movements. Currently, the AICoacher has a number of fitness courses supported by online coaches and has been tested on hundreds of fitness movements. The code can be downloaded from https://github.com/liutiel/AICoacher.

Vehicle scale estimation with a single camera is a typical application for intelligent transportation and it faces the challenges from visual computing while intensity-based method and descriptor-based method should be balanced. This paper proposed a vehicle scale estimation method based on salient object detection to resolve this problem. The regularized intensity matching method is proposed in Lie Algebra to achieve robust and accurate scale estimation, and descriptor matching and intensity matching are combined to minimize the proposed loss function. The visual attention mechanism is designed to select image patches with texture and remove the occluded image patches. Then the weights are assigned to pixels from the selected image patches which alleviates the influence of noise-corrupted pixels. The experiments show that the proposed method significantly outperforms state-of-the-art methods with regard to the robustness and accuracy of vehicle scale estimation.