An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-Identification

In recent years, a variety of proposed methods based on deep convolutional neural networks (CNNs) have improved the state of the art for large-scale person re-identification (ReID). While a large number of optimizations and network improvements have been proposed, there has been relatively little evaluation of the influence of training data and baseline network architecture. In particular, it is usually assumed either that networks are trained on labeled data from the deployment location (scene-dependent), or else adapted with unlabeled data, both of which complicate system deployment. In this paper, we investigate the feasibility of achieving scene-independent person ReID by forming a large composite dataset for training. We present an in-depth comparison of several CNN baseline architectures for both scene-dependent and scene-independent ReID, across a range of training dataset sizes. We show that scene-independent ReID can produce leading-edge results, competitive with unsupervised domain adaption techniques. Finally, we introduce a new dataset for comparing within-camera and across-camera person ReID.Comment: To be published in 2018 15th Conference on Computer and Robot Vision (CRV

ACRP Design Competition

According to the Federal Aviation Administration, there have been approximately two-hundred thousand civil aircraft collisions with wildlife during the last three decades in the United States alone. Ninety-seven percent of these collisions occurred during takeoff or landing. As of this writing, there is no definitive method used in order to prevent these “bird strikes”, which cause nearly one billion dollars in damages per year in the United States. The team aims to solve this problem. The goal is to integrate an autonomous drone into the daily workings of a local Rhode Island airport. The drone will patrol a designated route along the perimeter of airport grounds and deter birds from foraging or nesting in the area. The drone will be equipped with lights and sounds that are tuned specifically to disrupt bird behavior and communication, thus making the area undesirable. The specific lights and sounds integrated in the drones design are based upon information researched using web-based literary resources. The objective is to not only deter birds from airport grounds, but also to limit the distraction to pilots and airport staff. Implementing ultraviolet lights in the design helps to achieve this goal. Ultraviolet lights give off only trace amounts of light that can be seen by a human eye, but these wavelengths are fully within the bird’s visual spectrum. Therefore, it is possible to minimize the distraction to pilots and staff. Using the drone provided to the team by Professor Nassersharif, the team performed various tests to determine the most effective method of deterring birds. Equipping the drone with UV lights as well as a speaker that emits predatory bird sounds has proven to be the most efficient method. A flight path was also incorporated into the software to make the drone autonomous

An Exploration of Recent Intelligent Image Analysis Techniques for Visual Pavement Surface Condition Assessment.

Road pavement condition assessment is essential for maintenance, asset management, and budgeting for pavement infrastructure. Countries allocate a substantial annual budget to maintain and improve local, regional, and national highways. Pavement condition is assessed by measuring several pavement characteristics such as roughness, surface skid resistance, pavement strength, deflection, and visual surface distresses. Visual inspection identifies and quantifies surface distresses, and the condition is assessed using standard rating scales. This paper critically analyzes the research trends in the academic literature, professional practices and current commercial solutions for surface condition ratings by civil authorities. We observe that various surface condition rating systems exist, and each uses its own defined subset of pavement characteristics to evaluate pavement conditions. It is noted that automated visual sensing systems using intelligent algorithms can help reduce the cost and time required for assessing the condition of pavement infrastructure, especially for local and regional road networks. However, environmental factors, pavement types, and image collection devices are significant in this domain and lead to challenging variations. Commercial solutions for automatic pavement assessment with certain limitations exist. The topic is also a focus of academic research. More recently, academic research has pivoted toward deep learning, given that image data is now available in some form. However, research to automate pavement distress assessment often focuses on the regional pavement condition assessment standard that a country or state follows. We observe that the criteria a region adopts to make the evaluation depends on factors such as pavement construction type, type of road network in the area, flow and traffic, environmental conditions, and region\u27s economic situation. We summarized a list of publicly available datasets for distress detection and pavement condition assessment. We listed approaches focusing on crack segmentation and methods concentrating on distress detection and identification using object detection and classification. We segregated the recent academic literature in terms of the camera\u27s view and the dataset used, the year and country in which the work was published, the F1 score, and the architecture type. It is observed that the literature tends to focus more on distress identification ( presence/absence detection) but less on distress quantification, which is essential for developing approaches for automated pavement rating

DeepLight: Robust and unobtrusive real-time screen-camera communication for real-world displays

National Research Foundation (NRF) Singapore under NRF Investigatorship gran

Naval Reserve support to information Operations Warfighting

Since the mid-1990s, the Fleet Information Warfare Center (FIWC) has led the Navy's Information Operations (IO) support to the Fleet. Within the FIWC manning structure, there are in total 36 officer and 84 enlisted Naval Reserve billets that are manned to approximately 75 percent and located in Norfolk and San Diego Naval Reserve Centers. These Naval Reserve Force personnel could provide support to FIWC far and above what they are now contributing specifically in the areas of Computer Network Operations, Psychological Operations, Military Deception and Civil Affairs. Historically personnel conducting IO were primarily reservists and civilians in uniform with regular military officers being by far the minority. The Naval Reserve Force has the personnel to provide skilled IO operators but the lack of an effective manning document and training plans is hindering their opportunity to enhance FIWC's capabilities in lull spectrum IO. This research investigates the skill requirements of personnel in IO to verify that the Naval Reserve Force has the talent base for IO support and the feasibility of their expanded use in IO.http://archive.org/details/navalreservesupp109451098

Effective design, configuration, and use of digital CCTV

It is estimated that there are five million CCTV cameras in use today. CCTV is used by a wide range of organisations and for an increasing number of purposes. Despite this, there has been little research to establish whether these systems are fit for purpose. This thesis takes a socio-technical approach to determine whether CCTV is effective, and if not, how it could be made more effective. Humancomputer interaction (HCI) knowledge and methods have been applied to improve this understanding and what is needed to make CCTV effective; this was achieved in an extensive field study and two experiments. In Study 1, contextual inquiry was used to identify the security goals, tasks, technology and factors which affected operator performance and the causes at 14 security control rooms. The findings revealed a number of factors which interfered with task performance, such as: poor camera positioning, ineffective workstation setups, difficulty in locating scenes, and the use of low-quality CCTV recordings. The impact of different levels of video quality on identification and detection performance was assessed in two experiments using a task-focused methodology. In Study 2, 80 participants identified 64 face images taken from four spatially compressed video conditions (32, 52, 72, and 92 Kbps). At a bit rate quality of 52 Kbps (MPEG-4), the number of faces correctly identified reached significance. In Study 3, 80 participants each detected 32 events from four frame rate CCTV video conditions (1, 5, 8, and 12 fps). Below 8 frames per second, correct detections and task confidence ratings decreased significantly. These field and empirical research findings are presented in a framework using a typical CCTV deployment scenario, which has been validated through an expert review. The contributions and limitations of this thesis are reviewed, and suggestions for how the framework should be further developed are provided