In a visual sensor network, a large number of camera nodes are able to acquire and process image data locally, collaborate with other camera nodes and provide a description about the captured events. Typically, camera...
详细信息
ISBN:
(纸本)9781479957521
In a visual sensor network, a large number of camera nodes are able to acquire and process image data locally, collaborate with other camera nodes and provide a description about the captured events. Typically, camera nodes have severe constraints in terms of energy, bandwidth resources and processing capabilities. Considering these unique characteristics, coding and transmission of the pixel-level representation of the visual scene must be avoided, due to the energy resources required. A promising approach is to extract at the camera nodes, compact visual features that are coded to meet the bandwidth and power requirements of the underlying network and devices. Since the total number of features extracted from an image may be rather significant, this paper proposes a novel method to select the most relevant features before the actual coding process. The solution relies on a score that estimates the accuracy of each local feature. Then, local features are ranked and only the most relevant features are coded and transmitted. The selected features must maximize the efficiency of the image analysis task but also minimize the required computational and transmission resources. Experimental results show that higher efficiency is achieved when compared to the previous state-of-the-art.
Visual binarydescriptors have successfully been employed in several applications such as visual search, object recognition and visual tracking. In particular, binarydescriptors are suitable for scenarios where compu...
详细信息
ISBN:
(纸本)9781509003792
Visual binarydescriptors have successfully been employed in several applications such as visual search, object recognition and visual tracking. In particular, binarydescriptors are suitable for scenarios where computational, storage and energy resources are constrained and have been previously exploited to track an object along a video sequence. In this paper, binarydescriptors are used to perform visual tracking in a stereo-based system, i.e. when two cameras with overlapping views are employed in a cooperative way. The proposed stereo-based visual tracker follows the tracking-by-detection approach where features extracted from different cameras are used to characterize the object appearance with a suitable model. Moreover visual tracking is performed at a central controller by just using the features transmitted from the two camera nodes. To achieve this target, efficient coding techniques are proposed to reduce the amount of feature data that is transmitted through the network. The performance of the proposed stereo-based visual tracker is evaluated in terms of rate-accuracy, i.e. using quantitative metrics to assess the accuracy of the visual tracker as a function of the coding bitrate.
暂无评论