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Portrait cartoonization aims at translating a portrait image to its cartoon version, which guarantees two conditions, namely, reducing textural details and synthesizing cartoon facial features (e.g., big eyes or line-drawing nose). To address this problem, we propose a two-stage training scheme based on GAN, which is powerful for stylization problems. The abstraction stage with a novel abstractive loss is used to reduce textural details. Meanwhile, the perception stage is adopted to synthesize cartoon facial features. To comprehensively evaluate the proposed method and other state-of-the-art methods for portrait cartoonization, we contribute a new challenging large-scale dataset named CartoonFace10K. In addition, we find that the popular metric FID focuses on the target style yet ignores the preservation of the input image content. We thus introduce a novel metric FISI, which compromises FID and SSIM to focus on both target features and retaining input content. Quantitative and qualitative results demonstrate that our proposed method outperforms other state-of-the-art methods. 

Counting multi-vehicle motions via traffic cameras in urban areas is crucial for smart cities. Even though several frameworks have been proposed in this task, there is no prior work focusing on the highly common, dense and size-variant vehicles such as motorcycles. In this paper, we propose a novel framework for vehicle motion counting with adaptive label-independent tracking and counting modules that processes 12 frames per second. Our framework adapts hyperparameters for multi-vehicle tracking and properly works in complex traffic conditions, especially invariant to camera perspectives. We achieved the competitive results in terms of root-mean-square error and runtime performance.