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Hierarchical clustering
In this letter, a new superpixel-based constant-false-alarm-rate (CFAR) target detection algorithm for high-resolution synthetic aperture radar (SAR) images is proposed. The detection algorithm consists of three stages, i.e., segmentation, detection, and clustering. In the segmentation stage, a superpixel-generating algorithm is utilized to segment the SAR image. In the detection stage, based on the superpixels generated, the clutter distribution parameters for each pixel can be adaptively estimated, even in the multitarget situations. Then, the two-parameter CFAR test statistic can be adopted for detection. In the clustering stage, the hierarchical clustering is used to cluster the detected superpixels to get the candidate targets. The effectiveness of the proposed algorithm is demonstrated using the miniSAR data.
The vehicle routing problem (VRP) is an important transportation problem. The literature addresses several extensions of this problem, including variants having delivery time windows associated with customers and variants allowing split deliveries to customers. The problem extension including both of these variations has received less attention in the literature. This research effort sheds further light on this problem. Specifically, this paper analyzes the effects of combinations of local search (LS) move operators commonly used on the VRP and its variants. We find when paired with a MAX-MIN Ant System constructive heuristic, Or-opt or 2-opt⁎ appear to be the ideal LS operators to employ on the VRP with split deliveries and time windows with Or-opt finding higher quality solutions and 2-opt⁎ requiring less run time.
In this paper, we consider the problem of finding and localizing social human groups in videos, which can form a basis for further analysis and monitoring of groups in general. Our approach is motivated by the collective behavior of individuals which has a fundament in sociological studies. We design a detection-based multi-target tracking framework which is capable of handling short-term occlusions and producing stable trajectories. Human groups are discovered by clustering trajectories of individuals in an agglomerative fashion. A novel similarity function related to distances between group members, robustly measures the similarity of noisy trajectories. We have evaluated our approach on several test sequences and achieved acceptable miss rates (19.4%, 29.7% and 46.7%) at reasonable false positive detections per frame (0.129, 0.813 and 0.371). The relatively high miss rates are caused by a strict evaluation procedure, whereas the visual results are quite acceptable.
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