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Advanced Driving Assistance System (ADAS) can predict pedestrian's trajectory, in order to avoid traffic accidents and guarantee driving safety. A few current pedestrian trajectory prediction methods use a pedestrian's historical motion to predict the future trajectory, but the pedestrian's trajectory is also affected by the vehicle using the ADAS for prediction (target vehicle). Other studies predict the pedestrian's and vehicle's trajectories separately, and use the latter to adjust the former, but their interaction is a continuous process and should be considered during prediction rather than after. Therefore, we propose PVII, a pedestrian-vehicle interactive and iterative prediction framework for pedestrian's trajectory. It makes prediction for one iteration based on the results from previous iteration, which essentially models the vehicle-pedestrian interaction. In this iterative framework, to avoid accumulation of prediction errors along with the increased iterations, we design a bi-layer Bayesian en/decoder. For each iteration, it not only uses inaccurate results from previous iteration but also accurate historical data for prediction, and calculates Bayesian uncertainty values to evaluate the results. In addition, the pedestrian's trajectory is affected by both target vehicle and other vehicles around it (surrounding vehicle), so we include into the framework a pre-trained speed estimation module for surrounding vehicles (SE module). It estimates the speed based on pedestrian's motion and we collect data from pedestrian's view for training. In experiments, PVII can achieve the highest prediction accuracy compared to the current methods. [ABSTRACT FROM AUTHOR]

Automatic to locate the salient regions in the images are useful for many computer vision and computer graphics tasks. However, the previous techniques prefer to give noisy and fuzzy saliency maps, which will be a crucial limitation for the performance of subsequent image processing. In this paper, we present a novel framework by aggregating various bottom-up cues and bias to enhance visual saliency detection. It can produce high-resolution, full-field saliency map which can be close to binary one and more effective in real-world applications. First, the proposed method concentrates on multiple saliency cues in a global context, such as regional contrast, spatial relationship and color histogram smoothing to produce a coarse saliency map. Second, combining complementary boundary prior with smoothing, we iteratively refine the coarse saliency map to improve the contrast between salient and non-salient regions until a close to binary saliency map is reached. Finally, we evaluate our salient region detection on two publicly available datasets with pixel accurate annotations. The experimental results show that the proposed method performs equally or better than the 12 alternative methods and retains comparable detection accuracy, even in extreme cases. Furthermore, we demonstrate that the saliency map produced by our approach can serve as a good initialization for automatic alpha matting and image retargeting. [ABSTRACT FROM AUTHOR]

Spatial co-location pattern mining discovers the subsets of features whose events are frequently located together in geographic space. The current research on this topic adopts a threshold-based approach that requires users to specify in advance the thresholds of distance and prevalence. However, in practice, it is not easy to specify suitable thresholds. In this article, we propose a novel iterative mining framework that discovers spatial co-location patterns without predefined thresholds. With the absolute and relative prevalence of spatial co-locations, our method allows users to iteratively select informative edges to construct the neighborhood relationship graph until every significant co-location has enough confidence and eventually to discover all spatial co-location patterns. The experimental results on real world data sets indicate that our framework is effective for prevalent co-locations discovery. [ABSTRACT FROM AUTHOR]