Object tracking based on local learning

Li, Xiaohui; Lu, Huchuan

doi:10.1109/icip.2012.6466883

Cited by 6 publications

(1 citation statement)

References 14 publications

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“…11,12 Recently, learning-based approaches have been extensively investigated and exploited in a wide variety of applications, given its feature allowing highly complex, nonlinear modeling. [13][14][15][16] Among learning-based approaches, neural networks have gained increasing popularity due to their ability to learn the complex non-linear mapping between inputs and outputs. 17,18 The learning process of neural networks, however, is usually computationally intensive and may require several hours for training.…”

Section: Related Workmentioning

confidence: 99%

Autonomous target tracking of UAVs based on low-power neural network hardware

et al. 2014

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Detecting and identifying targets in unmanned aerial vehicle (UAV) images and videos have been challenging problems due to various types of image distortion. Moreover, the significantly high processing overhead of existing image/video processing techniques and the limited computing resources available on UAVs force most of the processing tasks to be performed by the ground control station (GCS) in an off-line manner. In order to achieve fast and autonomous target identification on UAVs, it is thus imperative to investigate novel processing paradigms that can fulfill the real-time processing requirements, while fitting the size, weight, and power (SWaP) constrained environment. In this paper, we present a new autonomous target identification approach on UAVs, leveraging the emerging neuromorphic hardware which is capable of massively parallel pattern recognition processing and demands only a limited level of power consumption. A proof-of-concept prototype was developed based on a micro-UAV platform (Parrot AR Drone) and the CogniMem TM neural network chip, for processing the video data acquired from a UAV camera on the fly. The aim of this study was to demonstrate the feasibility and potential of incorporating emerging neuromorphic hardware into next-generation UAVs and their superior performance and power advantages towards the real-time, autonomous target tracking.

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