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Fish-eye cameras are efficient means to provide an omni-view video recording over a large area using a single camera. Although effective algorithms for human detection in images captured by conventional cameras have been developed, human detection in fish-eye images remains an open challenge. Recognizing that humans typically appear on radial lines emitted from the center in fish-eye images, we propose...
For human action recognition methods, there is often a trade-off between classification accuracy and computational efficiency. Methods that include 3D information from multiple cameras are often computationally expensive and not suitable for real-time application. 2D, frame-based methods are generally more efficient, but suffer from lower recognition accuracies. In this paper, we present a hybrid...
In this paper, we present a two-stage framework that deal with the problem of automatically extract human activities from videos. First, for action recognition we employ an unsupervised state-of-the-art learning algorithm based on Independent Subspace Analysis (ISA). This learning algorithm extracts spatio-temporal features directly from video data and it is computationally more efficient and robust...
In this paper, we propose a framework which fuses multiple features for action recognition in depth sequence. The fusion of multiple features is important for recognizing action since a single feature-based representation is inadequate to capture the variants. Hence, we use two types of features: i) a quantized vocabulary of local spatio-temporal descriptor HOG3D, and ii) a global projection based...
This paper presents a concept for testing camera based ADAS, in order to reduce time and cost in the development phase. By adapting an existing virtual environment for the camera system, identifying and enhancing the important features for the testing detection function, the testing ADAS shall record the virtual driving scenes and deliver the same results as in real environments. A method to generate...
This paper explores the feasibility of using hyperspectral imagery for blueberry fruit detection. Some bands of hyperspectral images offer redundant information. A Kullback-Leibler divergence (KLD) based band selection method is used to select the most useful bands. Forty hyperspectral images of blueberry plants were taken from the field with 1-millimeter special resolution. The proposed KLD based...
The aim of this work is to provide a fast approach for monocular SLAM initialization by constructing an initial 3-D map with interest points that are susceptible to be automatically tracked. Interest points' depth is inferred by means of a linear regression model, which estimates depth on the basis of local image appearance. Our contributions are: (1) a new scheme for learning and predicting associations...
In this paper, we present a system for detecting pedestrians at long ranges using a combination of stereo-based detection, classification using deep learning, and a cascade of specialized classifiers that can reduce false positives and computational load. Specifically, we use stereo to perform detection of vertical structures which are further filtered based on edge responses. A convolutional neural...
In digital image forensics, camera model identification seeks for the source camera model information from the given images under investigation. To achieve this goal, one of the popular approaches is extracting from the images under investigation certain statistical features that capture the difference caused by camera structure and various in-camera image processing algorithms, followed by machine...
Solving the person re-identification problem has become important for understanding people's behaviours in a multicamera network of non-overlapping views. In this work, we address the problem of re-identification from a set-based verification perspective. More specifically, we have a small set of target people on a watch list (a set) and we aim to verify whether a query image of a person is on this...
Human activity recognition is central to many practical applications, ranging from visual surveillance to gaming interfacing. Most approaches addressing this problem are based on localized spatio-temporal features that can vary significantly when the viewpoint changes. As a result, their performances rapidly deteriorate as the difference between the viewpoints of the training and testing data increases...
Vision-based object detectors are crucial for different applications. They rely on learnt object models. Ideally, we would like to deploy our vision system in the scenario where it must operate. Then, the system should self-learn how to distinguish the objects of interest, i.e., without human intervention. However, the learning of each object model requires labelled samples collected through a tiresome...
This paper presents a fusing methodology through an illustration of how visual and compass sensors for location and route recognition can be combined. The data acquisition platform consists of two cameras, a compass sensor and several peripheral inputs such as a GPS and an accelerometer. With just the visual features and compass directions, a Fast Learning Artificial Neural Network (FLANN) is used...
In this paper, a new abnormal activity detection algorithm is proposed for multi-camera surveillance applications. The proposed algorithm models the entire scene covered by the multi-camera system as a network. In this network, each node corresponds to a segmentation of the entire scene and each edge represents the activity correlation between the corresponding segmentations. Based on this network,...
A novel algorithm for view-invariant human action recognition is presented. This approach is based on Two-Dimensional Principal Component Analysis (2DPCA) applied directly on the Motion Energy Image (MEI) or the Motion History Image (MHI) in both the spatial domain and the transform domain. This method reduces the computational complexity by a factor of at least 66, achieving the highest recognition...
This paper presents a study of cell-phone camera model linkage that matches digital images against potential makes / models of cell-phone camera sources using camera color interpolation features. The matching performance is examined and the dependency on the content of training image collection is evaluated via variance analysis. Training content dependency can be dealt with under the framework of...
We address the problem of recognizing offensive play strategies from American football play videos. Specifically, we propose a probabilistic model which describes the generative process of an observed football play and takes into account practical issues in real football videos, such as difficulty in identifying offensive players, view changes, and tracking errors. In particular, we exploit the geometric...
Detecting pedestrians in images is a key functionality to avoid vehicle-to-pedestrian collisions. The most promising detectors rely on appearance-based pedestrian classifiers trained with labelled samples. This paper addresses the following question: can a pedestrian appearance model learnt in virtual scenarios work successfully for pedestrian detection in real images? (Fig. 1). Our experiments suggest...
Early warning systems are critical in providing emergency response in the event of unexpected hazards. Cheap cameras and improvements in memory and computing power have enabled the design of fire detectors using video surveillance systems. This is critical in scenarios where traditional smoke detectors cannot be installed. In such scenarios, it has been observed that the smoke is visible well before...
In public venues, crowd size is a key indicator of crowd safety and stability. Crowding levels can be detected using holistic image features, however this requires a large amount of training data to capture the wide variations in crowd distribution. If a crowd counting algorithm is to be deployed across a large number of cameras, such a large and burdensome training requirement is far from ideal....
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