Evaluating small drone surveillance capabilities to enhance traffic conformance intelligence

The availability of cheap small physical drones that fly around and have a variety of visual and sensor networks attached invites investigation for work applications. In this research we assess the capability of a set of commercially available drones (VTOL) that cost less than $1000 (Cheap is a relative term and we consider anything less than$5000 relatively cheap). The assessment reviews the capability to provide secure and safe motor vehicle surveillance for conformance intelligence. The evaluation was conducted by initially estimating a set of requirements that would satisfy an ideal surveillance situation and then by comparing a sample of drone specifications. The search is for identifying a drone that is fit for purpose. The conclusion is that more than $1000 needs to be spent on the drone and the resources for effective observation but less than$3000 in total is sufficient for the work application. The result and the analysis of traditional surveillance networks suggests that such drones can provide a low entry risk for additional benefits; and intelligence to those responsible for compliance on our roads

Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection

Effective fusion of complementary information captured by multi-modal sensors (visible and infrared cameras) enables robust pedestrian detection under various surveillance situations (e.g. daytime and nighttime). In this paper, we present a novel box-level segmentation supervised learning framework for accurate and real-time multispectral pedestrian detection by incorporating features extracted in visible and infrared channels. Specifically, our method takes pairs of aligned visible and infrared images with easily obtained bounding box annotations as input and estimates accurate prediction maps to highlight the existence of pedestrians. It offers two major advantages over the existing anchor box based multispectral detection methods. Firstly, it overcomes the hyperparameter setting problem occurred during the training phase of anchor box based detectors and can obtain more accurate detection results, especially for small and occluded pedestrian instances. Secondly, it is capable of generating accurate detection results using small-size input images, leading to improvement of computational efficiency for real-time autonomous driving applications. Experimental results on KAIST multispectral dataset show that our proposed method outperforms state-of-the-art approaches in terms of both accuracy and speed

System Interface for an Integrated Intelligent Safety System (ISS) for Vehicle Applications

This paper deals with the interface-relevant activity of a vehicle integrated intelligent safety system (ISS) that includes an airbag deployment decision system (ADDS) and a tire pressure monitoring system (TPMS). A program is developed in LabWindows/CVI, using C for prototype implementation. The prototype is primarily concerned with the interconnection between hardware objects such as a load cell, web camera, accelerometer, TPM tire module and receiver module, DAQ card, CPU card and a touch screen. Several safety subsystems, including image processing, weight sensing and crash detection systems, are integrated, and their outputs are combined to yield intelligent decisions regarding airbag deployment. The integrated safety system also monitors tire pressure and temperature. Testing and experimentation with this ISS suggests that the system is unique, robust, intelligent, and appropriate for in-vehicle applications

Image Fusion Based on Nonsubsampled Contourlet Transform and Saliency-Motivated Pulse Coupled Neural Networks

In the nonsubsampled contourlet transform (NSCT) domain, a novel image fusion algorithm based on the visual attention model and pulse coupled neural networks (PCNNs) is proposed. For the fusion of high-pass subbands in NSCT domain, a saliency-motivated PCNN model is proposed. The main idea is that high-pass subband coefficients are combined with their visual saliency maps as input to motivate PCNN. Coefficients with large firing times are employed as the fused high-pass subband coefficients. Low-pass subband coefficients are merged to develop a weighted fusion rule based on firing times of PCNN. The fused image contains abundant detailed contents from source images and preserves effectively the saliency structure while enhancing the image contrast. The algorithm can preserve the completeness and the sharpness of object regions. The fused image is more natural and can satisfy the requirement of human visual system (HVS). Experiments demonstrate that the proposed algorithm yields better performance

Visual Human Tracking and Group Activity Analysis: A Video Mining System for Retail Marketing

Thesis (PhD) - Indiana University, Computer Sciences, 2007In this thesis we present a system for automatic human tracking and activity recognition from video sequences. The problem of automated analysis of visual information in order to derive descriptors of high level human activities has intrigued computer vision community for decades and is considered to be largely unsolved. A part of this interest is derived from the vast range of applications in which such a solution may be useful. We attempt to find efficient formulations of these tasks as applied to the extracting customer behavior information in a retail marketing context. Based on these formulations, we present a system that visually tracks customers in a retail store and performs a number of activity analysis tasks based on the output from the tracker. In tracking we introduce new techniques for pedestrian detection, initialization of the body model and a formulation of the temporal tracking as a global trans-dimensional optimization problem. Initial human detection is addressed by a novel method for head detection, which incorporates the knowledge of the camera projection model.The initialization of the human body model is addressed by newly developed shape and appearance descriptors. Temporal tracking of customer trajectories is performed by employing a human body tracking system designed as a Bayesian jump-diffusion filter. This approach demonstrates the ability to overcome model dimensionality ambiguities as people are leaving and entering the scene. Following the tracking, we developed a two-stage group activity formulation based upon the ideas from swarming research. For modeling purposes, all moving actors in the scene are viewed here as simplistic agents in the swarm. This allows to effectively define a set of inter-agent interactions, which combine to derive a distance metric used in further swarm clustering. This way, in the first stage the shoppers that belong to the same group are identified by deterministically clustering bodies to detect short term events and in the second stage events are post-processed to form clusters of group activities with fuzzy memberships. Quantitative analysis of the tracking subsystem shows an improvement over the state of the art methods, if used under similar conditions. Finally, based on the output from the tracker, the activity recognition procedure achieves over 80% correct shopper group detection, as validated by the human generated ground truth results