VLSI sensor for multiple targets detection and tracking

Fish, Alexander; Spivakovsky, Aleksander; Golberg, A.; Yadid-Pecht, Orly

doi:10.1109/icecs.2004.1399738

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…The inhibitory synapse provides a mechanism for the deselection of the winning neuron in favor of stimulus exploration. Hardware systems based on the concept of selective attention and WTA competition that lack such deselection mechanism have been specifically designed for tracking a selected target [ 14 , 40 ]. In such implementations, once a target is selected, the system is designed to lock on the target, disregarding distracters and even new salient stimuli.…”

Section: Selective Attention Chipmentioning

confidence: 99%

Selective Attention in Multi-Chip Address-Event Systems

Bartolozzi

Indiveri

2009

Sensors

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Selective attention is the strategy used by biological systems to cope with the inherent limits in their available computational resources, in order to efficiently process sensory information. The same strategy can be used in artificial systems that have to process vast amounts of sensory data with limited resources. In this paper we present a neuromorphic VLSI device, the “Selective Attention Chip” (SAC), which can be used to implement these models in multi-chip address-event systems. We also describe a real-time sensory-motor system, which integrates the SAC with a dynamic vision sensor and a robotic actuator. We present experimental results from each component in the system, and demonstrate how the complete system implements a real-time stimulus-driven selective attention model.

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Section: Selective Attention Chipmentioning

confidence: 99%

Selective Attention in Multi-Chip Address-Event Systems

Bartolozzi

Indiveri

2009

Sensors

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“…The proposed sensor can be useful in star tracking, machine vision and navigation applications. This implementation is a continuation of our previous work, in which we implemented a low-power multiple target tracking sensor based on biological models of attention [10]. Similar to the previous solution, the proposed sensor is designed in CMOS technology and includes several features, such as the capability of tracking multiple salient targets in the field of view by real time target centroid computation, low power dissipation, fully autonomous operation, operation in integration mode, high fill factor and high spatial resolution.…”

Section: Introductionmentioning

confidence: 97%

Autonomous CMOS image sensor for real time target detection and tracking

Teman

Fisher

Sudakov

et al. 2008

2008 IEEE International Symposium on Circuits and Systems (ISCAS)

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An autonomous image sensor for real time target detection and tracking is presented. The sensor is based on a CMOS APS array, equipped with in-pixel functionality and integrates analog and digital components to achieve autonomous operation with minimal power dissipation. The system employs a two-phased operation flow; during the initial acquisition stage, the digital controller detects and acquires the brightest targets in the field of view within a single frame and defines windows of interest (WOI) around the center of mass coordinates of each object. Subsequently, the system moves into the analog tracking mode during which all areas outside of the WOI are entirely shut down, thus saving power to a number of orders of magnitude. In addition to its low power dissipation, the sensor features real-time operation, low fixed pattern noise, linearity and the ability to track a predefined number of targets throughout the entire field of view. A 64x64 pixel sensor array has been designed in 0.18µm CMOS technology and is operated via a 1.8V supply. The imager architecture is discussed, the circuits' descriptions are shown and simulation results are presented.

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“…Optical flow estimation techniques [19] are also widely used in motion estimation that have a considerable amount of circuit level complexity. The winner-takes-all neural networks [20], sensor level processing [21] and analog neural networks [22] have found application in object detection with limited scalability.…”

Section: Introductionmentioning

confidence: 99%

Memristive Threshold Logic Circuit Design of Fast Moving Object Detection

Maan

Kumar²,

Sugathan³

et al. 2015

IEEE Trans. VLSI Syst.

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Real-time detection of moving objects involves memorisation of features in the template image and their comparison with those in the test image. At high sampling rates, such techniques face the problems of high algorithmic complexity and component delays. We present a new resistive switching based threshold logic cell which encodes the pixels of a template image. The cell comprises a voltage divider circuit that programs the resistances of the memristors arranged in a single node threshold logic network and the output is encoded as a binary value using a CMOS inverter gate. When a test image is applied to the templateprogrammed cell, a mismatch in the respective pixels is seen as a change in the output voltage of the cell. The proposed cell when compared with CMOS equivalent implementation shows improved performance in area, leakage power, power dissipation and delay.

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VLSI sensor for multiple targets detection and tracking

Cited by 5 publications

References 21 publications

Selective Attention in Multi-Chip Address-Event Systems

Selective Attention in Multi-Chip Address-Event Systems

Autonomous CMOS image sensor for real time target detection and tracking

Memristive Threshold Logic Circuit Design of Fast Moving Object Detection

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