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This paper proposes an event detection method using noisy object information. Some events have a close connection with objects, and the objects related to the event often appear with the event in a video. For example, if an event "Grooming an animal" appears in a video, an animal and people should appear in the video. If we detect the objects that have a close connection with the events,...
The Ordered Data Variability (ODV) technique is introduced into hybrid clutter map/L-CFAR technique, which is used to process the echoes on the clutter map spatially. The new method is called as Clutter Map/ODV (CM/ODV) technique. An analytic expression of detection probability in homogeneous environment is presented, and detection performance in three different situations in nonhomogeneous environment...
The use of multiple disparate platforms in many remote sensing and surveillance applications allows one to exploit the coherent information shared among all sensory systems thereby potentially reducing the risk of making single-sensory biased detection and classification decisions. This paper introduces a target detection method based upon multi-channel coherence analysis (MCA) framework which optimally...
While existing face recognition systems based on local features are robust to issues such as misalignment, they can exhibit accuracy degradation when comparing images of differing resolutions. This is common in surveillance environments where a gallery of high resolution mugshots is compared to low resolution CCTV probe images, or where the size of a given image is not a reliable indicator of the...
This paper present a new distributed constant false alarm rate (CFAR) detector based on automatic censoring technique. In the scheme, every local decision of individual detector, resulting from the comparison between its sample level and the estimation of the background based on GOSCA-CFAR (generalized order statistic, cell averaging-CFAR) algorithm, takes the value zero or one. Then, the fusion center...
Robust object detection is a critical skill for robotic applications in complex environments like homes and offices. In this paper we propose a method for using multiple cameras to simultaneously view an object from multiple angles and at high resolutions. We show that our probabilistic method for combining the camera views, which can be used with many choices of single-image object detector, can...
This paper describes in details the performance of an advanced detecting algorithm for multi-sensor target Track Before Detection (TBD) through the Hough Transform (HT). The detection algorithm employs the idea of using the Hough Transform for joint detection of linear trajectory targets. The polar modification of the TBD-HT approach is applied to a multi-sensor Polar Hough detector for multi-sensor...
In using image analysis to assist a driver to avoid obstacles on the road, traditional approaches rely on various detectors designed to detect different types of objects. We propose a framework that is different from traditional approaches in that it focuses on finding a clear path ahead. We assume that the video camera is calibrated offline (with known intrinsic and extrinsic parameters) and vehicle...
In this paper, we propose a real time pedestrian detection approach which consists of two levels: coarse detection and further validation. First, partial stages of cascaded Adaboost classifiers are adopted to detect the upper bodies and generate candidate regions with a high detection rate. In the second level, a probability template is proposed, based on which a template matching technique is used...
A hardware-friendly object detection algorithm has been developed using directional-edge histograms as key local-image-feature descriptors. In the recognition window, a number of small blocks having varying sizes and shapes are prepared for pattern matching. Four directional edges in vertical, horizontal and ±45 degrees are extracted from a local image enclosed in the block and their spatial distribution...
In this paper a technique of Hough detector threshold procedure for moving target detection in conditions of randomly arriving impulse interference with Poisson distributed flow and Raleigh amplitude distribution is proposed. The expressions of detection and false alarm probability are derived for a highly fluctuating Swerling II target. A comparative analysis of the performance of a Hough detector...
This paper introduces a new target detection method for multiple disparate sonar platforms. The detection method is based upon multi-channel coherence analysis (MCA) framework which allows one to optimally decompose the multichannel data to analyze their linear dependence or coherence. This decomposition then allows one to extract MCA features which can be used to discriminate between two hypotheses,...
In this paper, we present a novel probabilistic framework for automatic follicle quantification in 3D ultrasound data. The proposed framework robustly estimates size and location of each individual ovarian follicle by fusing the information from both global and local context. Follicle candidates at detected locations are then segmented by a novel database guided segmentation method. To efficiently...
This paper reports a method for acquiring the prior probability of human existence by using past human trajectories and the color of an image. The priors play important roles in human detection as well as in scene understanding. The proposed method is based on the assumption that a person can exist again in an area where he/she existed in the past. In order to acquire the priors efficiently, a high...
In this paper, our goal is to search for a novel object, where we have a prior map of the environment and knowledge of some of the objects in it, but no information about the location of the specific novel object. We develop a probabilistic model over possible object locations that utilizes object-object and object-scene context. This model can be queried for any of over 25,000 naturally occurring...
We explore the feasibility of low probability of intercept for sonar signals. Using a noise-like active sonar signal, the transmitter (platform) employs a matched filter for echo detection while the target is assumed to use an energy detector. Decision statistic distributions are developed at both the platform and target. These distributions allow efficient Monte Carlo simulation of detection performance...
The problem of detecting underwater targets from electro-optical (EO) images is considered in this paper. A block-based log-likelihood ratio test has been developed for detection and segmentation of underwater mine-like objects in the EO images captured with a CCD-based image sensor. The main focus of this research is to develop a robust detection algorithm that can be used to detect low contrast...
Detecting the presence/absence of an object in a region of interest is one of the important applications for sensor networks. A considerable amount of work has been seen in the literature for detecting events or objects using wireless sensor networks. Most of the prior work uses a simple binary detection model or an average signal strength model to make decisions of detection. Such methods are not...
In this paper we introduce a new switched order statistics CFAR test (SW-OS) for detecting a radar target in the presence of nonhomogeneous clutter and/or multiple interfering targets situation. Whereas a switching CFAR test (S-CFAR) was recently proposed in the literature for addressing a similar background scenario, unlike the S-CFAR test, the test proposed here does not utilize the test cell statistic...
This paper presents a new framework for detection of targets in clutter and noise. Conventional radar detection techniques involve testing each range cell separately at a predefined probability of false alarm. In this paper, we propose a novel surveillance paradigm that controls the false discovery rate (FDR) for a specified surveillance area (SA), which consists of a number of range cells. This approach...
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