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A self-driving car, to be deployed in real-world driving environments, must be capable of reliably detecting and effectively tracking of nearby moving objects. This paper presents our new, moving object detection and tracking system that extends and improves our earlier system used for the 2007 DARPA Urban Challenge. We revised our earlier motion and observation models for active sensors (i.e., radars...
We describe a real-time pedestrian detection system intended for use in automotive applications. Our system demonstrates superior detection performance when compared to many state-of-the-art detectors and is able to run at a speed of 14 fps on an Intel Core i7 computer when applied to 640×480 images. Our approach uses an analysis of geometric constraints to efficiently search feature pyramids and...
This paper presents a monocular vision based 3D bicycle tracking framework for intelligent vehicles based on a detection method exploiting a deformable part model and a tracking method using an Interacting Multiple Model (IMM) algorithm. Bicycle tracking is important because bicycles share the road with vehicles and can move at comparable speeds in urban environments. From a computer vision standpoint,...
Bicycles that share the road with intelligent vehicles present particular challenges for automated perception systems. Bicycle detection is important because bicycles share the road with vehicles and can move at comparable speeds in urban environments. From a computer vision standpoint, bicycle detection is challenging as bicycle's appearance can change dramatically between viewpoints and a person...
This paper presents a vision-based framework for intelligent vehicles to detect and track people riding bicycles in urban traffic environments. To deal with dramatic appearance changes of a bicycle according to different viewpoints as well as nonrigid nature of human appearance, a method is proposed which employs complementary detection and tracking algorithms. In the detection phase, we use multiple...
This paper presents a maximum likelihood estimator (MLE) for the ZigBee networks. We propose a deployment of cluster-tree topology in the ZigBee networks and derive the MLE under the log-normal models for the Received Signal Strength (RSS) measurements. The ZiLA algorithm is also proposed to apply the MLE to ZigBee networks. To validate the RSS measurement model, we have conducted exhaustive experiments...
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