Embedded Vehicle Speed Estimation System Using an Asynchronous Temporal Contrast Vision Sensor

Bauer, Dietmar; Belbachir, Ahmed Nabil; Donath, N.; Gritsch, G.; Kohn, Bernhard; Litzenberger, M.; Posch, C.; Schön, Peter; Schraml, Stephan

doi:10.1186/1687-3963-2007-082174

Cited by 21 publications

(20 citation statements)

References 5 publications

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“…One can imagine such a system integrated into the infrastructure of a smart traffic intersection, giving it the capability to detect and locate pedestrians, bikes, and cars within the intersection and communicate this information to new vehicles arriving on the scene, or to control timing of traffic signals. The ATIS has already found application in highway traffic monitoring [6] for car counting and speed estimation.…”

Section: Introductionmentioning

confidence: 99%

Real-time object recognition and orientation estimation using an event-based camera and CNN

Ghosh

Mishra

Orchard

et al. 2014

2014 IEEE Biomedical Circuits and Systems Conference (BioCAS) Proceedings

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Real-time visual identification and tracking of objects is a computationally intensive task, particularly in cluttered environments which contain many visual distracters. In this paper we describe a real-time bio-inspired system for object tracking and identification which combines an event-based vision sensor with a convolutional neural network running on FPGA for recognition. The event-based vision sensor detects only changes in the scene, naturally responding to moving objects and ignoring static distracters in the background. We present operation of the system for two tasks. The first is proof of concept for a remote monitoring application in which the system tracks and distinguishes between cars, bikes, and pedestrians on a road. The second task targets application to grasp planning for an upper limb prosthesis and involves detecting and identifying household objects, as well as determining their orientation relative to the camera. The second task is used to quantify performance of the system, which can discriminate between 8 different objects in 2.25 ms with accuracy of 99.10% and is able to determine object orientation with ±4.5 • accuracy in an additional 2.28 ms with accuracy of 97.76%.

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Section: Introductionmentioning

confidence: 99%

Real-time object recognition and orientation estimation using an event-based camera and CNN

Ghosh

Mishra

Orchard

et al. 2014

2014 IEEE Biomedical Circuits and Systems Conference (BioCAS) Proceedings

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“…The results obtained by the second method presented in table II, were achieved using an approach proposed by [13], being its accuracy mainly dependent on the location and size of the velocity sensors. The results achieved using the third method, shown on the same table, were obtained using a methodology presented in [14].…”

Section: Resultsmentioning

confidence: 98%

Ground plane velocity estimation embedding rectification on a particle filter multi-target tracking

Palaio

Maduro

Batista

et al. 2009

2009 IEEE International Conference on Robotics and Automation

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This paper presents an integrated solution for vehicle's velocity estimation and vehicle counting. The proposed restores the scene geometric properties, building a ground plane rectified image. Moreover, multiple vehicles tracking is performed embedding the concept of region covariance descriptors in a particle filter framework. The results show the effectiveness of the approach here proposed in very clutter scenes.

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“…Smart cameras are one example of this trend. While single cameras can be used to trigger events and support a human observer in a surveillance system [1] or perform vehicle detection and speed estimation [2], distributed smart camera networks offer an even higher benefit [3]. They can be used to detect obstacles to avoid collisions [4] or perform a cooperative tracking with local image analysis [5].…”

Section: Introductionmentioning

confidence: 99%

Reducing Processing Delay in Dataflow-oriented Middleware Systems for Smart Camera Applications

Guggi

Rinner

2014

Proceedings of the International Conference on Distributed Smart Cameras

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Dataflow-oriented processing is an attractive paradigm for smart camera applications. In this paper we present a datafloworiented middleware system to support the development of such applications on resource-limited distributed systems. Our main contribution includes the reduction of the overall processing delay by delaying the data generation and by the just-in-time transfer of data elements. A side effect of this delayed data transfer is also a reduction of the memory requirement for the communication links. We introduce the basic algorithm and present experimental results of our middleware system running in a distributed environment.

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Embedded Vehicle Speed Estimation System Using an Asynchronous Temporal Contrast Vision Sensor

Cited by 21 publications

References 5 publications

Real-time object recognition and orientation estimation using an event-based camera and CNN

Real-time object recognition and orientation estimation using an event-based camera and CNN

Ground plane velocity estimation embedding rectification on a particle filter multi-target tracking

Reducing Processing Delay in Dataflow-oriented Middleware Systems for Smart Camera Applications

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