Computer vision tools for the non-invasive assessment of autism-related behavioral markers

The early detection of developmental disorders is key to child outcome, allowing interventions to be initiated that promote development and improve prognosis. Research on autism spectrum disorder (ASD) suggests behavioral markers can be observed late in the first year of life. Many of these studies involved extensive frame-by-frame video observation and analysis of a child's natural behavior. Although non-intrusive, these methods are extremely time-intensive and require a high level of observer training; thus, they are impractical for clinical and large population research purposes. Diagnostic measures for ASD are available for infants but are only accurate when used by specialists experienced in early diagnosis. This work is a first milestone in a long-term multidisciplinary project that aims at helping clinicians and general practitioners accomplish this early detection/measurement task automatically. We focus on providing computer vision tools to measure and identify ASD behavioral markers based on components of the Autism Observation Scale for Infants (AOSI). In particular, we develop algorithms to measure three critical AOSI activities that assess visual attention. We augment these AOSI activities with an additional test that analyzes asymmetrical patterns in unsupported gait. The first set of algorithms involves assessing head motion by tracking facial features, while the gait analysis relies on joint foreground segmentation and 2D body pose estimation in video. We show results that provide insightful knowledge to augment the clinician's behavioral observations obtained from real in-clinic assessments

DETER: Detection of events for threat evaluation and recognition

The current security infrastructure can be summarized as follows: (1) Security systems act locally and do not cooperate in an effective manner, (2) Very valuable assets are protected inadequately by antiquated technology systems and (3) Security systems rely on intensive human concentration to detect and assess threats.In this paper we present DETER (Detection of Events for Threat Evaluation and Recognition), a research and development (R&D) project aimed to develop a high-end automated security system. DETER can be seen as an attempt to bridge the gap between current systems reporting isolated events and an automated cooperating network capable of inferring and reporting threats, a function currently being performed by humans.The prototype DETER system is installed at the parking lot of Honeywell Laboratories (HL) in Minneapolis. The computer vision module of DETER reliably tracks pedestrians and vehicles and reports their annotated trajectories to the threat assessment module for evaluation. DETER features a systematic optical and system design that sets it apart from "toy" surveillance systems. It employs a powerful Normal mixture model at the pixel level supported by an expectation-maximization (EM) initialization, the Jeffreys divergence measure, and the method of moments. It also features a practical and accurate multicamera. calibration method. The threat assessment module utilizes the computer vision information and can provide alerts for behaviors as complicated as the "hopping" of potential vehicle thieves from vehicle spot to vehicle spot.Extensive experimental results measured during actual field operations support DETER's exceptional characteristics. DETER has recently been successfully productized. The product-grade version of DETER monitors movements across the length of a new oil pipeline