Efficient background subtraction for tracking in embedded camera networks

Shen, Yiran; Hu, Wen Hua; Yang, Mingrui; Liu, Junbin; Chou, Chun Tung

doi:10.1145/2185677.2185698

Cited by 5 publications

(5 citation statements)

References 2 publications

(1 reference statement)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It creates opportunities for many applications based on camera sensor networks, which have received much attention recently in research [2,16,19,11,17]. One basic problem is how to characterize the usefulness of the image data and how to optimize the network to achieve better quality of information.…”

Section: Related Workmentioning

confidence: 99%

SmartPhoto: A Resource-Aware Crowdsourcing Approach for Image Sensing with Smartphones

Wang

et al. 2016

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

Photos obtained via crowdsourcing can be used in many critical applications. Due to the limitations of communication bandwidth, storage and processing capability, it is a challenge to transfer the huge amount of crowdsourced photos. To address this problem, we propose a framework, called SmartPhoto, to quantify the quality (utility) of crowdsourced photos based on the accessible geographical and geometrical information (called metadata) including the smartphone's orientation, position and all related parameters of the built-in camera. From the metadata, we can infer where and how the photo is taken, and then only transmit the most useful photos. Three optimization problems regarding the tradeoffs between photo utility and resource constraints, namely the Max-Utility problem, the online MaxUtility problem and the Min-Selection problem, are studied. Efficient algorithms are proposed and their performance bounds are theoretically proved. We have implemented SmartPhoto in a testbed using Android based smartphones, and proposed techniques to improve the accuracy of the collected metadata by reducing sensor reading errors and solving object occlusion issues. Results based on real implementations and extensive simulations demonstrate the effectiveness of the proposed algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

SmartPhoto: A Resource-Aware Crowdsourcing Approach for Image Sensing with Smartphones

Wang

et al. 2016

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

show abstract

“…Average color of each block is then computed and used for reducing computation costs. Similarly, the work in [30] is based on compressive sensing (CS), as opposed to getting the average color of each block. The idea of CS is simple and different compared to the process of traditional compressing for images.…”

Section: Motivationmentioning

confidence: 99%

Performance Comparison of Data Reduction Techniques for Wireless Multimedia Sensor Network Applications

Sarisaray-Boluk

Akkaya

2015

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

With the increased use of smart phones, Wireless Multimedia Sensor Networks (WMSNs) will have opportunities to deploy such devices in several contexts for data collection and processing. While smart phones come with richer resources and can do complex processing, their battery is still limited. Background subtraction (BS) and compression techniques are common data reduction schemes, which have been used for camera sensors to reduce energy consumption in WMSNs. In this paper, we investigate the performance of various BS algorithms and compression techniques in terms of computation and communication energy, time, and quality. We have picked five different BS algorithms and two compression techniques and implemented them in an Android platform. Considering the fact that these BS algorithms will be run within the context of WMSNs where the data is subject to packet losses and errors, we also investigated the performance in terms of packet loss ratio in the network under various packet sizes. The experiment results indicated that the most energy-efficient BS algorithm could also provide the best quality in terms of the foreground detected. The results also indicate that data reduction techniques including BS algorithms and compression techniques can provide significant energy savings in terms of transmission energy costs.

show abstract

Section: Related Workmentioning

confidence: 99%

SmartPhoto

Wang

et al. 2014

Proceedings of the 15th ACM International Symposium on Mobile Ad Hoc Networking and Computing

View full text Add to dashboard Cite

Photos obtained via crowdsourcing can be used in many critical applications. Due to the limitations of communication bandwidth, storage and processing capability, it is a challenge to transfer the huge amount of crowdsourced photos. To address this problem, we propose a framework, called SmartPhoto, to quantify the quality (utility) of crowd-sourced photos based on the accessible geographical and geometrical information (called metadata) including the smart-phone's orientation, position and all related parameters of the built-in camera. From the metadata, we can infer where and how the photo is taken, and then only transmit the most useful photos. Three optimization problems regarding the tradeoffs between photo utility and resource constraints , namely the Max-Utility problem, the online Max-Utility problem and the Min-Selection problem, are studied. Efficient algorithms are proposed and their performance bounds are theoretically proved. We have implemented SmartPhoto in a testbed using Android based smart-phones, and proposed techniques to improve the accuracy of the collected metadata by reducing sensor reading errors and solving object occlusion issues. Results based on real implementations and extensive simulations demonstrate the effectiveness of the proposed algorithms.

show abstract

Efficient background subtraction for tracking in embedded camera networks

Cited by 5 publications

References 2 publications

SmartPhoto: A Resource-Aware Crowdsourcing Approach for Image Sensing with Smartphones

SmartPhoto: A Resource-Aware Crowdsourcing Approach for Image Sensing with Smartphones

Performance Comparison of Data Reduction Techniques for Wireless Multimedia Sensor Network Applications

SmartPhoto

Contact Info

Product

Resources

About