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In this paper we present an autonomous detection approach for airborne surveillance in maritime scenarios. This approach is robust to sun glare, waves and scale variation. Additionally, we introduce a new metric to evaluate detection and tracking results that is more adequate for these scenarios. The proposed detection method is evaluated using videos from different monitoring missions and its results...
Non-maximum suppression is an integral part of the object detection pipeline. First, it sorts all detection boxes on the basis of their scores. The detection box M with the maximum score is selected and all other detection boxes with a significant overlap (using a pre-defined threshold) with M are suppressed. This process is recursively applied on the remaining boxes. As per the design of the algorithm,...
In this work we show an approximation for the detection and false alarm probabilities of a differential detector with constant false alarm probability, applied to GNSS signal acquisition. Also, a novel performance metric is shown that we believe is an important parameter when designing efficient acquisition strategies for the implementation of GNSS receivers. The work allows for the conclusion that...
The rapid advances of transportation infrastructure have led to a dramatic increase in the demand for smart systems capable of monitoring traffic and street safety. Fundamental to these applications are a community-based evaluation platform and benchmark for object detection and multi-object tracking. To this end, we organize the AVSS2017 Challenge on Advanced Traffic Monitoring, in conjunction with...
Many games use dynamic difficulty adjustment (DDA) to promote the achievement of flow and consequent positive affective states. However, performance based DDA assume a specific ludic attitude: that of the hard-core gamer. An alternative approach is to apply affective computing techniques to monitor players adjust difficulty to achieve the desired affective state directly. Such an emotion-controlled...
Event detection in unconstrained videos is conceived as a content-based video retrieval with two modalities: textual and visual. Given a text describing a novel event, the goal is to rank related videos accordingly. This task is zero-exemplar, no video examples are given to the novel event. Related works train a bank of concept detectors on external data sources. These detectors predict confidence...
We propose a method for multi-person detection and 2-D pose estimation that achieves state-of-art results on the challenging COCO keypoints task. It is a simple, yet powerful, top-down approach consisting of two stages. In the first stage, we predict the location and scale of boxes which are likely to contain people, for this we use the Faster RCNN detector. In the second stage, we estimate the keypoints...
This paper presents a novel large-scale dataset and comprehensive baselines for end-to-end pedestrian detection and person recognition in raw video frames. Our baselines address three issues: the performance of various combinations of detectors and recognizers, mechanisms for pedestrian detection to help improve overall re-identification (re-ID) accuracy and assessing the effectiveness of different...
Matching local image descriptors is a key step in many computer vision applications. For more than a decade, hand-crafted descriptors such as SIFT have been used for this task. Recently, multiple new descriptors learned from data have been proposed and shown to improve on SIFT in terms of discriminative power. This paper is dedicated to an extensive experimental evaluation of learned local features...
The remote detection, identification and quantification of chemical vapor plumes in the atmosphere are difficult problems that have been addressed using longwave hyperspectral sensors. Chemical detection algorithms compare the sensor's spectral measurements to a library of known chemical signatures to decide whether or not a plume is present. Detection of the chemical plume is an important step in...
In the past decade, research in person re-identification (re-id) has exploded due to its broad use in security and surveillance applications. Issues such as inter-camera viewpoint, illumination and pose variations make it an extremely difficult problem. Consequently, many algorithms have been proposed to tackle these issues. To validate the efficacy of re-id algorithms, numerous benchmarking datasets...
Differential spatial modulation (DSM) is a novel attractive alternative technique for coherent spatial modulation (CSM) without channel state information (CSI) at the receiver. In this paper, iterative detection is firstly employed to improve the performance of DSM schemes. With the Hamming distance of two matrices reduced to the sum of simple elements, a lowcomplexity iterative detection scheme for...
Space-time trellis codes (STTCs) combine channel coding and multiple-input multiple-output (MIMO) techniques to provide coding and diversity gains for wireless communication systems. The decoding complexity is extremely high because of the high density of branch metric calculations. Thus, this study presents a state-purging mechanism based on the T-algorithm to reduce the computational complexity...
In this paper we propose a technique to deal with unknown random jitter in a band-limited Gaussian channel. The jitter caused by the deviation or displacement of signal pulses affects the performance of the communication system. We show that a sampling set as small as twice the baud rate is enough for good detection performance. Detection is done by means of a suboptimal algorithm with polynomial...
In this letter, we propose target detector version of recently introduced basic thresholding classifier for hyperspectral images. The proposed technique is a sparsity-based low complexity detector which achieves high detection rates with very low false alarm rates and performs extremely rapidly. We also propose a new decision metric, background model, and spatial smoothing procedure in order to increase...
Two anomaly detectors for control systems are analyzed with respect to their sensitivity to malicious data injection attacks. A stateless anomaly detector based on the current residual signal is compared to a cumulative sum detector. The worst-case impact of a stealthy time-limited data injection attack is characterized for both detectors by a non-convex optimization problem and compared to determine...
In this study, the motion blur caused by the variable speed egomotion of camera is deblurred using multiple image frames and the obtained results are compared based on the detection performance of corner features. A linear, uniform motion blur data set is collected which is suitable for testing the multi-image deblurring method. The proposed method is tested on the dataset together with two baseline...
Pest camouflages in grains or natural environment cause significant difficulties in pest detection using imaging technologies. This paper proposes a convolutional Riemannian texture with differential entropic active contours to distinguish the background regions and expose pest regions. An image texture model is firstly introduced on the Riemannian manifold. A convolutional Riemannian texture structure...
Detection of corner is the most essential process in a large number of computer vision and image processing applications. We have mentioned a number of popular contour-based corner detectors in our paper. Among all these detectors chord to triangular arm angle (CTAA) has been demonstrated as the most dominant corner detector in terms of average repeatability. We introduce a new effective method to...
The estimation of blurred regions is an important stage in several computer vision applications. In this paper an efficient training-free detector of local blurriness based on edge features is presented. Due to the intrinsic sparsity of edges in natural images a blur map is creating by using an approach based on the heat diffusion principle. A 2D point discrete Poisson solver is concatenated with...
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