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Typically, in a detector framework, the model size is fixed at the size of the smallest object to be detected, and larger objects are detected by scaling the input image. The information lost due to scaling could be vital for accurately detecting large objects, which is an essential task for vision-based driver-assistance systems. To this end, we evaluate a(More)
This paper undergoes a finer-grained analysis of current state-of-the-art in pedestrian detection, with the aims of discovering insights into why and when detection fails. Current pedestrian detection research studies are often measured and compared by a single summarizing metric across datasets. The progress in the field is measured by comparing the metric(More)
We investigate a new strategy for improving localization accuracy of detected vehicles using a deep convolutional neural network. Specifically, we implement an iterative bounding box refinement on top of a state-of-the-art object detector. The bounding box refinement is achieved by iteratively pooling features from previous object location predictions. On(More)
Highly accurate, camera-based object detection is an essential component of autonomous navigation and assistive technologies. In particular, for on-road applications, localization quality of objects in the image plane is important for accurate distance estimation, safe trajectory prediction, and motion planning. In this paper, we mathematically formulate(More)
Vehicle detection is an essential task in an intelligent vehicle. Despite being a well-studied vision problem, it is unclear how well vehicle detectors generalize to new settings. Specifically, this paper studies the generalization capability of vehicle detectors on a U.S. highway dataset. Two types of models are employed in the experimental analysis, a(More)
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