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Traffic light recognition is a key technology for intelligent vehicles and Advanced Driver Assistance Systems (ADAS). This paper proposes a multi-feature fusion based real-time traffic light recognition algorithm for intelligent vehicles. In the region of interest determined by the vanishing line, technologies including color segmentation, blob detection, and structural feature extraction are employed...
We propose a novel method to segment the moving object in video clips. In this work, we introduce a region trajectory generation model based on graph clustering. Point trajectories are widely used to measure the motion similarity because of their unambiguity. However, region trajectories preserve object boundaries, while optical flow based point trajectories always ‘over-smooth’ to the background...
In this paper, we present a method to extract moving objects in monocular image sequences. The proposed method is based on graph cuts defined on a spatio-temporal region adjacency graph (RAG). First, we initially over-segment each frame in the video, and take the over-segmented regions as the vertices in the 3D spatio-temporal graph. Second, multiple cues are fused together to extract objects accurately...
In this paper, we present a biased sampling strategy for object class modeling, which can effectively circumvent the scene matching problem commonly encountered in statistical image-based object categorization. The method optimally combines the bottom-up, biologically inspired saliency information with loose, top-down class prior information to form a probabilistic distribution for feature sampling...
Road detection is a crucial part of autonomous driving system. Most of the methods proposed nowadays only achieve reliable results in relatively clean environments. In this paper, we combine edge detection with road area extraction to solve this problem. Our method works well even on noisy campus road whose boundaries are blurred with sidewalks and surface is often covered with unbalanced sunlight...
In this paper, we propose a novel framework of object categorization, namely layered object categorization, which takes advantage of hierarchical category information and performs object categorization at different levels. The proposed hierarchical structure of object categories is built bottom-up and top-down simultaneously accordingly to cognitive rules. First, part-based models are learnt to evaluate...
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