A new Real-Time Hardware Architecture for Road Line Tracking Using a Particle Filter

Alarcon, J.; Salvador, Rubén; Moreno, Félix; Cobos, Pesántez; López, Irene

doi:10.1109/iecon.2006.347566

Cited by 14 publications

(12 citation statements)

References 11 publications

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“…3. The control of the layers has been implemented so that each layer is self-controlled but directed by the previous one, giving the system a general control strategy implemented in a distributed fashion [8]. This makes it possible for each module to have control over itself, but making the overall system work coordinately among the different modules, allowing the system to show the emergeney of an incipient intelligent behavior (distributed intelligence).…”

Section: Specialized Tnnsmentioning

confidence: 99%

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Reconfigurable Hardware Architecture of a Shape Recognition System Based on Specialized Tiny Neural Networks With Online Training

Moreno¹,

Alarcon²,

Salvador³

et al. 2009

IEEE Trans. Ind. Electron.

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Abstract-Neural networks are widely used in pattern recognition, security applications, and robot control. We propose a hardware architecture system using tiny neural networks (TNNs) specialized in image recognition. The generic TNN architecture allows for expandability by means of mapping several basic units (layers) and dynamic reconflguration, depending on the application speciflc demands. One of the most important features of TNNs is their learning ability. Weight modiflcation and architecture reconflguration can be carried out at run-time. Our system performs objects identiflcation by the interpretation of characteristics elements of their shapes. This is achieved by interconnecting several specialized TNNs. The results of several tests in different conditions are reported in this paper. The system accurately detects a test shape in most of the experiments performed. This paper also contains a detailed description of the system architecture and the processing steps. In order to valídate the research, the system has been implemented and conflgured as a perceptron network with back-propagation learning, choosing as reference application the recognition of shapes. Simulation results show that this architecture has signiflcant performance beneflts.

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Section: Specialized Tnnsmentioning

confidence: 99%

“…The target application of this paper is the correct identiflcation of road trafflc signs in images taken by a car-mounted camera [8], [9]. The basic technique used for this in most applications, known as pattern matching, is to compare each portion of an image with a set of known models.…”

Section: Introductionmentioning

confidence: 99%

Reconfigurable Hardware Architecture of a Shape Recognition System Based on Specialized Tiny Neural Networks With Online Training

Moreno¹,

Alarcon²,

Salvador³

et al. 2009

IEEE Trans. Ind. Electron.

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“…For example, [5] has presented a hardware architecture using particle filters to track road lines. However, in their implementation the usual samplingimportance-resampling processing chain is missing, and the detailed parallelization are not discussed.…”

Section: Related Workmentioning

confidence: 99%

Algorithm and architecture for object tracking using particle filter

Wang

Chang

Chen

2009

2009 IEEE International Conference on Multimedia and Expo

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Particle filtering is a popular algorithm for vision-based target tracking. Despite its effectiveness in many fields of tracking, however, the computation requirement of particle filters is high. In this paper we propose an algorithm and architecture for vision-based particle filters. The proposed algorithm can estimate objects' positions, sizes, and angles by using color histogram as the feature. We propose two hardware parallelization schemes -one makes use of particle-level parallel operation and the other utilizes particle-level plus pixel-level parallel operation. The experimental result shows the tracking accuracy of our test sequences is over 85%, and the proposed hardware architecture can run the particle filtering in real-time, with 31.35 frames per second in average.

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“…The target application of our work is the correct identification of road traffic signs in images taken by a car mounted camera, [8], [9]. This paper shows the on-going work towards low cost FPGA-based object detection and recognition systems.…”

Section: Introductionmentioning

confidence: 99%

Approach to an FPGA embedded, autonomous object recognition system: run-time learning and adaptation

et al. 2009

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Neural networks, widely used in pattern recognition, security applications and robot control have been chosen for the task of object recognition within this system. One of the main drawbacks of the implementation of traditional neural networks in reconfigurable hardware is the huge resource consuming demand. This is due not only to their intrinsic parallelism, but also to the traditional big networks designed. However, modern FPGA architectures are perfectly suited for this kind of massive parallel computational needs. Therefore, our proposal is the implementation of Tiny Neural Networks, TNN -self-coined term-, in reconfigurable architectures. One of most important features of TNNs is their learning ability. Therefore, what we show here is the attempt to rise the autonomy features of the system, triggering a new learning phase, at run-time, when necessary. In this way, autonomous adaptation of the system is achieved. The system performs shape identification by the interpretation of object singularities. This is achieved by interconnecting several specialized TNN that work cooperatively. In order to validate the research, the system has been implemented and configured as a perceptron-like TNN with backpropagation learning and applied to the recognition of shapes. Simulation results show that this architecture has significant performance benefits.

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A new Real-Time Hardware Architecture for Road Line Tracking Using a Particle Filter

Cited by 14 publications

References 11 publications

Reconfigurable Hardware Architecture of a Shape Recognition System Based on Specialized Tiny Neural Networks With Online Training

Reconfigurable Hardware Architecture of a Shape Recognition System Based on Specialized Tiny Neural Networks With Online Training

Algorithm and architecture for object tracking using particle filter

Approach to an FPGA embedded, autonomous object recognition system: run-time learning and adaptation

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