22 research outputs found
Energy-efficient Foreground Object Detection on Embedded Smart Cameras by Hardware-level Operations
No full textEmbedded smart cameras have limited processing power, memory and energy. In this paper, we introduce two methodologies to increase the energy-efficiency and the battery-life of an embedded smart camera by hardware level operations when performing foreground object detection. We use the CITRIC platform as our embedded smart camera. We first perform down-sampling at hardware level on the micro-controller of the image sensor rather than performing software-level down-sampling at the main microprocessor of the camera board. In addition, we crop an image frame at hardware level by using the HREF and VSYNC signals at the micro-controller of the image sensor to perform foreground object detection only in the cropped search region instead of the whole image. Thus, the amount of data that is moved from the image sensor to the main memory at each frame, is greatly reduced. Thanks to reduced data transfer, better use of the memory resources and not occupying the main microprocessor with image down-sampling and cropping tasks, we obtain significant savings in energy consumption and battery-life. Experimental results show that hardware-level down-sampling and cropping, and performing detection in cropped regions provide 54.14% decrease in energy consumption, and 121.25% increase in battery-life compared to performing software-level down sampling and processing whole frames
Energy-efficient Feedback Tracking on Embedded Smart Cameras by Hardware-level Optimization
No full textEmbedded systems have limited processing power, memory and energy. When camera sensors are added to an embedded system, the problem of limited resources becomes even more pronounced. In this paper, we introduce two methodologies to increase the energy-efficiency and battery-life of an embedded smart camera by hardware-level operations when performing object detection and tracking. The CITRIC platform is employed as our embedded smart camera. First, down-sampling is performed at hardware level on the micro-controller of the image sensor rather than performing software-level down-sampling at the main microprocessor of the camera board. In addition, instead of performing object detection and tracking on whole image, we first estimate the location of the target in the next frame, form a search region around it, then crop the next frame by using the HREF and VSYNC signals at the micro-controller of the image sensor, and perform detection and tracking only in the cropped search region. Thus, the amount of data that is moved from the image sensor to the main memory at each frame is optimized. Also, we can adaptively change the size of the cropped window during tracking depending on the object size. Reducing the amount of transferred data, better use of the memory resources, and delegating image down-sampling and cropping tasks to the micro-controller on the image sensor, result in significant decrease in energy consumption and increase in battery-life. Experimental results show that hardware-level down-sampling and cropping, and performing detection and tracking in cropped regions provide 41.24% decrease in energy consumption, and 107.2% increase in battery-life. Compared to performing software-level down-sampling and processing whole frames, proposed methodology provides an additional 8 hours of continuous processing on 4 AA batteries, increasing the lifetime of the camera to 15.5 hours
Energy-efficient Object Detection and Tracking on Embedded Smart Cameras by Hardware-level Operations at the Image Sensor
No full textEmbedded smart cameras have limited processing power, memory and energy. In this paper, we introduce two methodologies to increase the energy-efficiency and the battery-life of an embedded smart camera by hardware-level operations when performing object detection and tracking. We use the CITRIC platform as our embedded smart camera. We first perform down-sampling at hardware-level on the microcontroller of the image sensor rather than performing software-level down-sampling at the main microprocessor of the camera board. In addition, instead of performing object detection on whole image, we first estimate the location of the target in the next frame, form a search region around it, then crop the next frame by using the HREF and VSYNC signals at the microcontrollerof the image sensor, and perform detection and tracking only in the cropped search region. Thus, the amount of data that is moved from the image sensor to the main memory at each frame, is greatly reduced. Thanks to reduced data transfer, better use of the memory resources and not occupying the main microprocessor with image down-sampling and cropping tasks, we obtain significant savings in energy consumption and battery-life. Experimental results show that hardware-level down-sampling and cropping, and performing detection in cropped regions provide 54:14% decrease in energy consumption, and 121:25% increase in battery-life compared to performing software-level downsampling and processing whole frame
Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing
No full textWe address the problem of sequentially selecting and observing processes from a given set to find the anomalies among them. The decision-maker observes a subset of the processes at any given time instant and obtains a noisy binary indicator of whether or not the corresponding process is anomalous. We develop an anomaly detection algorithm that chooses the processes to be observed at a given time instant, decides when to stop taking observations, and declares the decision on anomalous processes. The objective of the detection algorithm is to identify the anomalies with an accuracy exceeding the desired value while minimizing the delay in decision making. We devise a centralized algorithm where the processes are jointly selected by a common agent as well as a decentralized algorithm where the decision of whether to select a process is made independently for each process. Our algorithms rely on a Markov decision process defined using the marginal probability of each process being normal or anomalous, conditioned on the observations. We implement the detection algorithms using the deep actor-critic reinforcement learning framework. Unlike prior work on this topic that has exponential complexity in the number of processes, our algorithms have computational and memory requirements that are both polynomial in the number of processes. We demonstrate the efficacy of these algorithms using numerical experiments by comparing them with state-of-the-art methods.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Signal Processing System
Frame-level temporal calibration of video sequences from unsynchronized cameras by using projective invariants
No full textMultiple object tracking and occlusion handling by information exchange between uncalibrated cameras
No full textIEEE Transactions on Information Theory: Vol. 59, No. 2, February 2013
No full text1. MMSE of "Bad" Codes / R. Bustin, S. Shamai
2. Converse Coding Theorems for Identification via Channels / Y. Oohama
3. Computable Bounds for Rate Distortion with Feed Forward for Stationary and Ergodic Sources / I. Naiss, H. H. Permuter
4. Classification of Homogeneous Data with Large Alphabets / B. G. Kelly, A. B. Wagner, T. Tularak, P. Viswanath
5. Guesswork, Large Deviations and Shannon Entropy / M. M. Christiansen, K. R. Duffy
6. Entropic Inequalities and Marginal Problems / T. Fritz, R. Chaves
7. On MMSE Crossing Properties and Implications in Parallel Vector Gaaussian Channels / R. Bustin, M. Payaro, D. P. Palomor
8. The Approximate Capacity of the Gaussian N-Relay Diamond Network / U. Niesen, S. N. Diggavi
9. Half-Duplex Relaying Over Slow Pading Channels Based on Quantize-and-Forward / S. Yao, T. T. Kim, M. Skoglund, H. V. Poor
10. Effective Capacity of Two-Hop Wireless Communication Systems / D. Qiao, M. C. Gursoy, S. Velipasalar
11. Capacity Bounds and Exact Results for the Cognitive Z-Interference Channel / N. Liu, I. Maric, A. J. Goldsmith, S. Shamai
12. Futher Results on the Asymptotic Mutual Information of Rician Fading MIMO Channels / G. Tricco
13. Multicarrier Beamforming With Limited Feedback: A Rate Distortion Approach / M. Xu, D. Guo, M. L. Honig
14. Capacity of DNA Data Embedding Under Substitution Mutations / F. Balado
15. Capacity of a Diffusion-Based Molecular Communication System With Channel Memory and Molecular Noise / M. Pierobon, I. F. Akyildiz
Etc
Specifying, Interpreting and Detecting High-level, Spatio-Temporal Composite Events in Single and Multi-Camera Systems
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