Vehicle Surround Capture: Survey of Techniques and a Novel Omni-Video-Based Approach for Dynamic Panoramic Surround Maps

Gandhi, T.; Trivedi, Mohan M.

doi:10.1109/tits.2006.880635

Cited by 84 publications

(48 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As research advances in the area of holistic sensing for driver safety and assistance systems, we will continue to see a growing fusion of technologies including omnidirectional video processing [25], [26], speech recognition [13], driver behavior analysis [10], [11], [12], and object detection [27], [28]. We believe the smartphone platform is a valuable addition to a holistic DAS, not only because of its advanced sensors and ability to recognize driving style, but also because of its access to global networks via its alwayson internet connection.…”

Section: Discussionmentioning

confidence: 99%

Driving style recognition using a smartphone as a sensor platform

Johnson

Trivedi

2011

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

596

310

View full text Add to dashboard Cite

Abstract-Driving style can characteristically be divided into two categories: "typical" (non-aggressive) and aggressive. Understanding and recognizing driving events that fall into these categories can aid in vehicle safety systems. Potentiallyaggressive driving behavior is currently a leading cause of traffic fatalities in the United States. More often than not, drivers are unaware that they commit potentially-aggressive actions daily. To increase awareness and promote driver safety, we are proposing a novel system that uses Dynamic Time Warping (DTW) and smartphone based sensor-fusion (accelerometer, gyroscope, magnetometer, GPS, video) to detect, recognize and record these actions without external processing. Our system differs from past driving pattern recognition research by fusing related inter-axial data from multiple sensors into a single classifier. It also utilizes Euler representation of device attitude (also based on fused data) to aid in classification. All processing is done completely on the smartphone.

show abstract

Section: Discussionmentioning

confidence: 99%

Driving style recognition using a smartphone as a sensor platform

Johnson

Trivedi

2011

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

596

310

View full text Add to dashboard Cite

show abstract

“…This work can be beneficial to the general intelligent vehicle public, and can be combined with such computer vision safety systems as have been described in [9], [10].…”

Section: Related Researchmentioning

confidence: 99%

Lidar based off-road negative obstacle detection and analysis

Larson¹,

Trivedi²

2011

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

Abstract-In order for an autonomous unmanned ground vehicle (UGV) to drive in off-road terrain at high speeds, it must analyze and understand its surrounding terrain in realtime: it must know where it intends to go, where are the hazards, and many details of the topography of the terrain. Much research has been done in the way of obstacle avoidance, terrain classification, and path planning, but still so few UGV systems can accurately traverse off-road environments at high speeds autonomously. One of the most dangerous hazards found offroad are negative obstacles, mainly because they are so difficult to detect. We present algorithms that analyze the terrain using a point cloud produced by a 3D laser range finder, then attempt to classify the negative obstacles using both a geometry-based method we call the Negative Obstacle DetectoR (NODR) as well as a support vector machine (SVM) algorithm. The terrain is analyzed with respect to a large UGV with the sensor mounted up high as well as a small UGV with the sensor mounted low to the ground.

show abstract

“…Omni-directional vision system have been previously used in intelligent vehicle applications, such as vehicle tracking, indoor parking lot, and driver monitoring driver, etc [8]. These applications used different omni-directional sensors, such as wide Field-Of-View(FOV) dioptric cameras, catadioptric cameras, Pan-Title-Zoom (PTZ) cameras and polydioptric cameras.…”

Section: The Problem and Proposed Approachmentioning

confidence: 99%

“…The surrounding of a vehicle will be captured by an omni-directional vision system on the top of a vehicle. In order to obtain high quality of surrounding images, we use an omni-directional vision system consisting of multiple cameras [7], rather than a catadioptric camera used by the most existing system for intelligent vehicles [8]. The video stream from the camera is processed to detect nearby vehicles and obstacles.…”

Section: Introductionmentioning

confidence: 99%

Enhancing a Driver's Situation Awareness using a Global View Map

Cheng

Liu

Zheng

2007

Multimedia and Expo, 2007 IEEE International Conference On

View full text Add to dashboard Cite

This paper proposes a novel method to enhance a driver's situation awareness by dynamically providing a global view of surroundings for the driver. The surroundings of a vehicle are captured by an omni-directional vision system mounted on the top of the vehicle. The video stream from the camera is processed to detect nearby vehicles. Positions of these detected objects are overlaid on a global view of a local map (e.g., an aerial imagery or satellite imagery map). We establish the relationship between the omni-directional vision system and the global view map. The global view map dynamically provides a realistic perspective view of the driving environment. This map can be projected onto an HUD on the windshield. By looking at the display, a driver can have a global picture of the situation and potentially produce a good driving strategy. We illustrate the proposed method by dynamically mapping a video stream onto Google Earth map.

show abstract

Vehicle Surround Capture: Survey of Techniques and a Novel Omni-Video-Based Approach for Dynamic Panoramic Surround Maps

Cited by 84 publications

References 43 publications

Driving style recognition using a smartphone as a sensor platform

Driving style recognition using a smartphone as a sensor platform

Lidar based off-road negative obstacle detection and analysis

Enhancing a Driver's Situation Awareness using a Global View Map

Contact Info

Product

Resources

About