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Classification of human actions is very challenging and important in many video-based applications. Two common features, i.e., the hand-crafted and the deep-learned ones are usually adopted for video representation and have been proven to be effective in many famous datasets in the literature. However, the hand-crafted feature lacks the ability to detect the discriminative and semantic features and...
In this paper, we propose an integrated system for scale-variance pedestrian detection. It consists of two cascaded components: a multi-layer detection neural network (MLDNN) for scale-variance pedestrian detection, and a fast decision forest (FDF) for boosting detection performance with only a slight decrease in speed. Experimental results on the Caltech Pedestrian dataset show that our approach...
This paper presents an innovative approach to investigate the inner mechanism between traffic status and crash potential based on High Definition Monitoring Systems (HDMS) data. HDMS records delicate vehicle trajectory data and characteristic details. Matched case-control method and Support Vector Machines (SVMs) were employed to identify risk status. The grid search method was utilized to find the...
In many real world applications, human analysts are not only interested in the detected anomalies but are also interested in the reasons behind why they were flagged as anomalous. However, existing anomaly detectors provide the analysts with no information about what caused the anomalies. A sequential feature explanation(SFE) of a detected data point is an ordered sequence of features which are presented...
We present a method to improve the accuracy of a foot-mounted, zero-velocity-aided inertial navigation system (INS) by varying estimator parameters based on a real-time classification of motion type. We train a support vector machine (SVM) classifier using inertial data recorded by a single foot-mounted sensor to differentiate between six motion types (walking, jogging, running, sprinting, crouch-walking,...
SCADA systems, an acronym for Supervisory Control And Data Acquisition (supervisory, Control and data acquisition), are control networks that allow the monitoring and management of industrial processes remotely. In the beginning, their top priority was the availability of information bidirectionally between the control station and the remote units; however, the growing escalation of industrial systems,...
In this paper we present an adversary-aware double JPEG detector which is capable of detecting the presence of two JPEG compression steps even in the presence of heterogeneous processing and counter-forensic (C-F) attacks. The detector is based on an SVM classifier fed with a large number of features and trained to recognise the traces left by double JPEG detection in the presence of attacks. Since...
This research proposes a reliable machine learning based computational solution for human detection. The proposed model is specifically applicable for illumination-variant natural scenes in big data video frames. In order to solve the illumination variation problem, a new feature set is formed by extracting features using histogram of gradients (HoG) and linear phase quantization (LPQ) techniques,...
Traffic anomaly detection is primarily concerned with identifying malicious traffic patterns in a much larger stream of benign traffic. Traditionally, this is achieved by selecting a very specialized set of traffic-based features that are used for both training a model, as well as for detection at runtime. This paper introduces a novel method of anomaly detection that breaks the assumption that the...
Most existing weakly supervised localization (WSL) approaches learn detectors by finding positive bounding boxes based on features learned with image-level supervision. However, those features do not contain spatial location related information and usually provide poor-quality positive samples for training a detector. To overcome this issue, we propose a deep self-taught learning approach, which makes...
In this work, we firstly apply support vector machine (SVM) detector in 122-Gbps Multi-CAP system. It can de-map the rotated constellations directly without any correction. The concrete simulations indicate such a machine-learning based detector provides considerable BER reduction for high-density CAPs in low-frequency band, compared with hard decision.
This paper proposes efficient real time method for sterile zone monitoring with human verification. The propose method consists of two main parts: Motion detection module and human verification module. The role of motion detection module is to segment out foreground object from background. Probabilistic Foreground Detector based on Gaussian Mixture Model(GMM) is used. Region of interest (ROI) obtained...
In today world the necessity for the autonomous mobile robots and vehicles is increasing. The safety autonomous moving demands the reliable and fast detection algorithms. The Histogram of Oriented Gradients (HOG) descriptors show significantly outperforms the existing feature sets for a human detection. Though the given method has a lot of type I errors. The amount of these errors can be decreased...
Traffic sign detection and recognition systems are essential components of Advanced Driver Assistance Systems and self-driving vehicles. In this contribution we present a vision-based framework which detects and recognizes traffic signs inside the attentional visual field of drivers. This technique takes advantage of the driver 3D absolute gaze point obtained through the combined use of a front-view...
Describing the contents of images is a challenging task for machines to achieve. It requires not only accurate recognition of objects and humans, but also their attributes and relationships as well as scene information. It would be even more challenging to extend this process to identify falls and hazardous objects to aid elderly or users in need of care. This research makes initial attempts to deal...
The problem of object localization in image appear ubiquitously in computer vision applications including image classification, object detection and visual tracking. Recently, it is shown that multiple-instance learning(MIL) which is regarded as the fourth machine learning framework compared with supervised learning, unsupervised learning and reinforce learning has been verified that will get good...
Specific characteristics of the functional near infrared spectroscopy (fNIRS) of the hemodynamic response may represent the brain cortical activity levels during mental arithmetic tasks. In this paper, we use hemodynamic response signals of the prefrontal cortex, acquired by a 4-channel fNIRS system to identify the difficulty level of an arithmetic task. To this end, twelve temporal features and several...
In computerized detection of clustered microcalcifications (MCs) from mammogram images the occurrence of false positives (FPs) varies greatly from case to case. In this work, we develop a probabilistic modeling approach to estimate the number of individual FPs present in a detected MC lesion. We describe the number of true positives (TPs) by a Poisson-Binomial probability distribution, wherein a logistic...
In this paper, we consider the problem of falls risk prediction in elderly adults using smartphone-based inertial gait measurements. We begin by collecting a parallel data set from a pressure sensitive walkway and smartphones. The walk-way data is used to calculate the falls risk ground truth using well-established biomechanical norms. The smartphone data and falls risk labels are then used to train...
Contextual information such as the co-occurrence of objects and the location of objects has played an important role in object detection. We present candidate pruning and object rescoring methods that leverage contextual information and that can improve the state-of-the-art CNN-based object detection methods such as Fast R-CNN and Faster R-CNN. In our pruning method, we formulate candidate reduction...
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