| 1. |
- Baroffio, L., et al.
(författare)
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Demo : Enabling image analysis tasks in visual sensor networks
- 2014
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Ingår i: Proceedings of the 8th ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2014. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450329255 ; , s. a46-
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Konferensbidrag (refereegranskat)abstract
- This demo showcases some of the results obtained by the GreenEyes project, whose main objective is to enable visual analysis on resource-constrained multimedia sensor networks. The demo features a multi-hop visual sensor network operated by BeagleBones Linux computers with IEEE 802.15.4 communication capabilities, and capable of recognizing and tracking objects according to two different visual paradigms. In the traditional compress-then-analyze (CTA) paradigm, JPEG compressed images are transmitted through the network from a camera node to a central controller, where the analysis takes place. In the alternative analyze-then-compress (ATC) paradigm, the camera node extracts and compresses local binary visual features from the acquired images (either locally or in a distributed fashion) and transmits them to the central controller, where they are used to perform object recognition/tracking. We show that, in a bandwidth constrained scenario, the latter paradigm allows to reach better results in terms of application frame rates, still ensuring excellent analysis performance.
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| 2. |
- Gu, Irene Yu-Hua, 1953, et al.
(författare)
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Employing Particle Filters on Riemannian Manifolds for Online Domain-Shift Object Learning and Occlusion Handling
- 2014
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Ingår i: 8th ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2014; Venezia; Italy; 4 November 2014 through 7 November 2014. - New York, NY, USA : ACM. - 9781450329255 ; , s. Art. no. a37-
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Konferensbidrag (refereegranskat)abstract
- Visual object tracking from single cameras is often employedas the basic block in a multi-camera tracking environment,and its performance naturally a--cts the multi-camera tracking system. Online learning of object model is essential for mitigating the tracking drift for highly dynamic video objects. This paper describes a domain-shift online learning and geodesic-based occlusion handling method for enhancing the robustness of manifold object tracking, especially when a large-size object (relative to an image-size) contains signifiant out-of-plane changes along with some long-term partial occlusion. The main contributions of the domain shift online learning method include: (a) Utilizing a particle filter on the manifold for online learning; (b) Bayesian formulation on the manifold, for posterior state estimation on the manifold based on nonlinear state space modeling; (c) A geodesic-based method for occlusion handling on the manifold, for preventing learning occluding objects/ clutter. The online learning method uses covariance matrices of manifold candidate objects (or, particles) at each time instant rather than from a sliding-window of objects in the conventional case, hence possibility of fast online learning. The proposed method has been applied to Riemannian manifold tracking of video objects that contain large-size objects with significant out-of-plane changes accompanied with long-term partial occlusions. The method is tested, compared and evaluated on a range of videos, results have provided strong support to the robustness of the proposed method. Discussions on computational issue and application scenario to multi-camera environment are also included.
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| 3. |
- Imran, Muhammad, et al.
(författare)
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Demo: SRAM FPGA based Wireless Smart Camera: SENTIOF-CAM
- 2014
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Ingår i: Proceedings of the International Conference on Distributed Smart Cameras. - New York, NY, USA : ACM. - 9781450329255
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Konferensbidrag (refereegranskat)abstract
- Wireless Sensor Networks applications with huge amount of datarequirements are attracting the utilization of high performanceembedded platforms i.e. Field Programmable Gate Arrays(FPGAs) for in-node sensor processing. However, the designcomplexity, high configuration and static energies of SRAMFPGAs impose challenges for duty cycled applications. In thisdemo, we demonstrate the functionality of SRAM FPGA basedwireless vision sensor node called SENTIOF-CAM. Thedemonstration shows that by using intelligent techniques, a lowenergy and low complexity SRAM FPGA based wireless visionsensor node can be realized for duty cycled applications.
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| 4. |
- Imran, Muhammad, et al.
(författare)
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Energy Driven Selection and Hardware Implementation of Bi-Level Image Compression
- 2014
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Ingår i: Proceedings of the International Conference on Distributed Smart Cameras. - New York, NY, USA : ACM Press. - 9781450329255
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Konferensbidrag (refereegranskat)abstract
- Wireless Vision Sensor Nodes are considered to have smaller resources and are expected to have a longer lifetime based on the available limited energy. A wireless Vision Sensor Node (VSN) is often characterized to consume more energy in communication as compared to processing. The communication energy can be reduced by reducing the amount of transmission data with the help of a suitable compression scheme. This work investigates bi-level compression schemes including G4, G3, JBIG2, Rectangular, GZIP, GZIP_Pack and JPEG-LS on a hardware platform. The investigation results show that GZIP_pack, G4 and JBIG2 schemes are suitable for a hardware implemented VSN. JBIG2 offers up to a 43 percent reduction in overall energy consumption as compared to G4 and GZIP_pack for complex images. However, JBIG2 has higher resource requirement and implementation complexity. The difference in overall energy consumption is smaller for smooth images. Depending on the application requirement, the exclusion of a header can reduce the energy consumption by approximately 1 to 33 percent.
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| 5. |
- Yun, Yixiao, 1987, et al.
(författare)
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Human Activity Recognition in Images Using SVMs and Geodesics on Smooth Manifolds
- 2014
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Ingår i: 8th ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2014; Venezia; Italy; 4 November 2014 through 7 November 2014. - New York, NY, USA : ACM. - 9781450329255 ; , s. Art. no. a20-
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Konferensbidrag (refereegranskat)abstract
- This paper addresses the problem of human activity recognition in still images. We propose a novel method that focuses on human-object interaction for feature representation of activities on Riemannian manifolds, and exploits underlying Riemannian geometry for classification. The main contributions of the paper include: (a) represent human activity by appearance features from local patches centered at hands containing interacting objects, and by structural features formed from the detected human skeleton containing the head, torso axis and hands; (b) formulate SVM kernel function based on geodesics on Riemannian manifolds under the log-Euclidean metric; (c) apply multi-class SVM classifier on the manifold under the one-against-all strategy. Experiments were conducted on a dataset containing 17196 images in 12 classes of activities from 4 subjects. Test results, evaluations, and comparisons with state-of-the-art methods provide support to the effectiveness of the proposed scheme.
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