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Development of smart cities has grasped much attention in research community and industry as well. Smart healthcare, communication, infrastructure are required for the development of smart cities. Security is one of the major concern in the development of smart cities. Automatic surveillance helps in boosting security in multiple areas like traffic, hospitals, schools, and industries etc. Video camera...
Row-wise exposure delay present in CMOS cameras is responsible for skew and curvature distortions known as the rolling shutter (RS) effect while imaging under camera motion. Existing RS correction methods resort to using multiple images or tailor scene-specific correction schemes. We propose a convolutional neural network (CNN) architecture that automatically learns essential scene features from a...
As an automatic tracking system, the shipboard Automatic Identification System (AIS) has been widely adopted to identify and locate the vessels by electronically exchanging data with other nearby ships. With the development of computer technology, AIS-based visualization of vessel traffic has attracted increasing attention during the past several years. The vessel density visualization can be used...
Action recognition has been one of the most popular fields of computer vision. This paper presents a novel approach to action recognition problem using the dimension reduction method, local fisher discriminant analysis, to reduce the dimension of feature descriptors as the preprocessing step after feature extraction. We propose to use sparse matrix and randomized kd-tree to modify and accelerate the...
This paper develops a framework for determining the Remaining Useful Life (RUL) of aero-engines. The framework includes the following modular components: creating a moving time window, a suitable feature extraction method and a multi-layer neural network as the main machine learning algorithm. The proposed framework is evaluated on the publicly available C-MAPSS dataset. The prognostic accuracy of...
Human activity recognition is a fundamental problem in computer vision with many applications such as video retrieval, automatic visual surveillance and human computer interaction. Sports represent one of the most viewed content on digital tv and the web. Automatically collected statistics of team sports game play represent actionable information for many end users such as coaches and broadcast speakers...
Trajectories extracted by previous methods for human action recognition contain irrelevant changes, and the Orientation-Magnitude descriptors of their shapes lack the robustness to camera motion. To solve these problems, action recognition by tracking salient relative motion points is proposed in this paper. Firstly, motion boundary detector which suppresses the camera constant motion is utilized...
We present a new approach for feature pooling in human action recognition. Instead of partitioning videos at predefined uniform intervals in a spatial-temporal volume as done with spatial pyramid matching, our method adaptively partitions in a pooling attribute space, defined by multiple trajectory-based cues. The pooling attributes include individual spatial and temporal coordinates of a trajectory,...
Human action recognition is widely recognized as a challenging task due to the difficulty of effectively characterizing human action in a complex scene. Recent studies have shown that the dense-trajectory-based methods can achieve state-of-the-art recognition results on some challenging datasets. However, in these methods, each dense trajectory is often represented as a vector of coordinates, consequently...
Screen touch gesture has been shown to be a promising modality for touch-based active authentication of users of mobile devices. In this paper, we present an approach for active user authentication using screen touch gestures by building linear and kernelized dictionaries based on sparse representations and associated classifiers. Experiments using a new dataset collected by us as well as two other...
We propose a method for classifying actions involving people interacting with objects. Our method combines motion and appearance information into a unified framework. Here, we explore the video's sparse component as provided by robust principal-component analysis for the extraction of motion information in the form of trajectories. While we use motion as the main clue for classification, we also incorporate...
In this paper we propose a new method for view-invariant gesture recognition, based on what we call nonparametric shape descriptor. We represent gestures as 3D motion trajectories and then we prove that the shape of a trajectory is equivalent to the Euclidean distances between all its points. The set of point-to-point distances description is mapped to a high-dimensional kernel space by kernel principal...
We present a method by which a robot learns to predict effective contact locations for pushing as a function of object shape. The robot performs push experiments at many contact locations on multiple objects and records local and global shape features at each point of contact. Each trial attempts to either push the object in a straight line or to rotate the object to a new orientation. The robot observes...
Event detection in crowded surveillance videos is a challenging yet important problem. This paper focuses on pair-wise events that involve the interaction of two persons (e.g., people embrace, meet or split) in crowded videos. To detect such an event accurately, we should build an effective representation model that can characterize the sequential properties of two persons' interaction. Towards this...
Action recognition is an important computer vision problem that has many applications including video indexing and retrieval, event detection, and video summarization. In this paper, we propose to apply the Fisher kernel paradigm to action recognition. The Fisher kernel framework combines the strengths of generative and discriminative models. In this approach, given the trajectories extracted from...
With increasing amounts of data being generated by businesses and researchers, there is a need for fast, accurate and robust algorithms for data analysis. Improvements in database's technology, computing performance and artificial intelligence have contributed to the development of intelligent data analysis. The primary aim of data mining is knowledge discovery, i.e. patterns in the data that lead...
In this paper, an overall framework for crowd analysis is presented. Detection and tracking of pedestrians as well as detection of dense crowds is performed on image sequences to improve simulation models of pedestrian flows. Additionally, graph-based event detection is performed by using Hidden Markov Models on pedestrian trajectories utilizing knowledge from simulations. Experimental results show...
A novel method for object tracking in videos for drinking activity recognition is proposed. The query object is detected in the first video frame, extracting a new query image. The obtained query image is then compared with patches within a determined region of interest around the position of the detected object in the previous frame. For each image, the local steering kernels are extracted and the...
Human action recognition has been well studied recently, but recognizing the activities of more than three persons remains a challenging task. In this paper, we propose a motion trajectory based method to classify human group activities. Gaussian Processes are introduced to represent human motion trajectories from a probabilistic perspective to handle the variability of people's activities in group...
We propose a method for detecting precursors, such as small rock and/or soil fall, which occur prior to massive slope failure. The key feature of our method is directly recognizing the trajectory of a small collapse using spatiotemporal Gabor filtering. Simulation analysis, where the conditions of the simulation are quantitatively defined, reveals the effectiveness of the proposed method in detecting...
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