Towards A Self-calibrating Video Camera Network For Content Analysis And Forensics

Due to growing security concerns, video surveillance and monitoring has received an immense attention from both federal agencies and private firms. The main concern is that a single camera, even if allowed to rotate or translate, is not sufficient to cover a large area for video surveillance. A more general solution with wide range of applications is to allow the deployed cameras to have a non-overlapping field of view (FoV) and to, if possible, allow these cameras to move freely in 3D space. This thesis addresses the issue of how cameras in such a network can be calibrated and how the network as a whole can be calibrated, such that each camera as a unit in the network is aware of its orientation with respect to all the other cameras in the network. Different types of cameras might be present in a multiple camera network and novel techniques are presented for efficient calibration of these cameras. Specifically: (i) For a stationary camera, we derive new constraints on the Image of the Absolute Conic (IAC). These new constraints are shown to be intrinsic to IAC; (ii) For a scene where object shadows are cast on a ground plane, we track the shadows on the ground plane cast by at least two unknown stationary points, and utilize the tracked shadow positions to compute the horizon line and hence compute the camera intrinsic and extrinsic parameters; (iii) A novel solution to a scenario where a camera is observing pedestrians is presented. The uniqueness of formulation lies in recognizing two harmonic homologies present in the geometry obtained by observing pedestrians; (iv) For a freely moving camera, a novel practical method is proposed for its self-calibration which even allows it to change its internal parameters by zooming; and (v) due to the increased application of the pan-tilt-zoom (PTZ) cameras, a technique is presented that uses only two images to estimate five camera parameters. For an automatically configurable multi-camera network, having non-overlapping field of view and possibly containing moving cameras, a practical framework is proposed that determines the geometry of such a dynamic camera network. It is shown that only one automatically computed vanishing point and a line lying on any plane orthogonal to the vertical direction is sufficient to infer the geometry of a dynamic network. Our method generalizes previous work which considers restricted camera motions. Using minimal assumptions, we are able to successfully demonstrate promising results on synthetic as well as on real data. Applications to path modeling, GPS coordinate estimation, and configuring mixed-reality environment are explored

Depthwise Separable Convolutional Neural Networks for Pedestrian Attribute Recognition

Video surveillance is ubiquitous. In addition to understanding various scene objects, extracting human visual attributes from the scene has attracted tremendous traction over the past many years. This is a challenging problem even for human observers. This is a multi-label problem, i.e., a subject in a scene can have multiple attributes that we are hoping to recognize, such as shoes types, clothing type, wearing some accessory, or carrying some object or not, etc. Solutions have been presented over the years and many researchers have employed convolutional neural networks (CNNs). In this work, we propose using Depthwise Separable Convolution Neural Network (DS-CNN) to solve the pedestrian attribute recognition problem. The network employs depthwise separable convolution layers (DSCL), instead of the regular 2D convolution layers. DS-CNN performs extremely well, especially with smaller datasets. In addition, with a compact network, DS-CNN reduces the number of trainable parameters while making learning efficient. We evaluated our method on two benchmark pedestrian datasets and results show improvements over the state of the art

A multi-branch separable convolution neural network for pedestrian attribute recognition

© 2020 The Authors Computer science; Computer Vision; Image processing; Deep learning; Pedestrian attribute recognitio

Visual Pseudo Haptics for a Dynamic Squeeze / Grab Gesture in Immersive Virtual Reality

In this work, we analyze the suitability of employing visual feedback for pseudo haptics as a replacement for active haptics in an immersive virtual reality (VR) environment. A controller-free gesture interaction is widely considered to be a natural user interface in VR. As the controller is not employed, the lack of active haptic feedback can often result in a frustrating experience for complex dynamic gestures, e.g., grab, squeeze, clasp, etc. These actions are very easy to perform using specialized devices or controllers with active haptic feedback, e.g., data gloves with force feedback or controllers with analog triggers and vibrations can be utilized for immediate or continuous feedback. In contrast, these mechanisms are completely missing in a controller-free interaction. We present an on-screen visual mechanism as the pseudo haptic feedback of a dynamic squeeze/grab gesture to replace the active haptic feedback. Our proposed approach allows for the continuous visualization of a squeeze/grab gesture. We implemented an interaction mechanism to test the visualization for these dynamic gestures and compared it with a system with no pseudo haptics. The results from the user study show that an on-screen continuous visualization can be used as pseudo haptics for a dynamic squeeze/grab gesture in an immersive VR environment

Selective subtraction for handheld cameras

© 2013 IEEE. Background subtraction techniques model the background of the scene using the stationarity property and classify the scene into two classes namely foreground and background. In doing so, most moving objects become foreground indiscriminately, except in dynamic scenes (such as those with some waving tree leaves, water ripples, or a water fountain), which are typically \u27learned\u27 as part of the background using a large training set of video data. We introduce a novel concept of background as the objects other than the foreground, which may include moving objects in the scene that cannot be learned from a training set because they occur only irregularly and sporadically, e.g. a walking person. We propose a \u27selective subtraction\u27 method as an alternative to standard background subtraction, and show that a reference plane in a scene viewed by two cameras can be used as the decision boundary between foreground and background. In our definition, the foreground may actually occur behind a moving object. Furthermore, the reference plane can be selected in a very flexible manner, using for example the actual moving objects in the scene, if needed. We extend this idea to allow multiple reference planes resulting in multiple foregrounds or backgrounds. We present diverse set of examples to show that: 1) the technique performs better than standard background subtraction techniques without the need for training, camera calibration, disparity map estimation, or special camera configurations; 2) it is potentially more powerful than standard methods because of its flexibility of making it possible to select in real-time what to filter out as background, regardless of whether the object is moving or not, or whether it is a rare event or a frequent one. Furthermore, we show that this technique is relatively immune to camera motion and performs well for hand-held cameras

Impact of In-Service Training on Performance of Teachers A Case of STEVTA Karachi Region

Learning which takes place in a classroom is significantly associated with teachers and their actions taken in the classroom. Therefore, quality of education can be improved by putting more focus on teaching methodologies and the way teachers spend time in classrooms. This study aimed at examining the impact of in-service training on the performance of the teachers. It is generally believed that with the implementation of certain in-service training programmes the performance of teachers regarding their professional skills, knowledge and experience can be signif icantly improved. The target population of the present study included the in-service teachers offering their services at Sindh Technical Education & Vocational Training Authority (STEVTA), Government of Sindh, Karaschi Region. Using close-ended questions, perception and experience of teachers (n=150, m=100, f=50), who availed the opportunity to get in-service training, were gained. Findings of the study revealed the positive impact of in-service training programmes on the performance of teachers. The study also revealed the positive perception of teachers regarding their professional growth. It recommended the in-service training programmes to be introduced in line with the subject rather than general

Crowd Modeling using Temporal Association Rules

Understanding crowd behavior has attracted tremendous attention from researchers over the years. In this work, we propose an unsupervised approach for crowd scene modeling and anomaly detection using association rules mining. Using object tracklets, we identify events occurring in the scene, demonstrated by the paths or routes objects take while traversing the scene. Allen\u27s interval-based temporal logic is used to extract frequent temporal patterns from the scene. Temporal association rules are generated from these frequent temporal patterns. Our goal is to understand the scene grammar, which is encoded in both the spatial and spatio-temporal patterns. We perform anomaly detection and test the method on a well-known public data

Impact of In-Service Training on Performance of Teachers A Case of STEVTA Karachi Region

Learning which takes place in a classroom is significantly associated with teachers and their actions taken in the classroom. Therefore, quality of education can be improved by putting more focus on teaching methodologies and the way teachers spend time in classrooms. This study aimed at examining the impact of in-service training on the performance of the teachers. It is generally believed that with the implementation of certain in-service training programmes the performance of teachers regarding their professional skills, knowledge and experience can be signif icantly improved. The target population of the present study included the in-service teachers offering their services at Sindh Technical Education & Vocational Training Authority (STEVTA), Government of Sindh, Karaschi Region. Using close-ended questions, perception and experience of teachers (n=150, m=100, f=50), who availed the opportunity to get in-service training, were gained. Findings of the study revealed the positive impact of in-service training programmes on the performance of teachers. The study also revealed the positive perception of teachers regarding their professional growth. It recommended the in-service training programmes to be introduced in line with the subject rather than general