Watch and Learn: Semi-Supervised Learning of Object Detectors from Videos

We present a semi-supervised approach that localizes multiple unknown object instances in long videos. We start with a handful of labeled boxes and iteratively learn and label hundreds of thousands of object instances. We propose criteria for reliable object detection and tracking for constraining the semi-supervised learning process and minimizing semantic drift. Our approach does not assume exhaustive labeling of each object instance in any single frame, or any explicit annotation of negative data. Working in such a generic setting allow us to tackle multiple object instances in video, many of which are static. In contrast, existing approaches either do not consider multiple object instances per video, or rely heavily on the motion of the objects present. The experiments demonstrate the effectiveness of our approach by evaluating the automatically labeled data on a variety of metrics like quality, coverage (recall), diversity, and relevance to training an object detector.Comment: To appear in CVPR 201

The Use of HepRep in GLAST

HepRep is a generic, hierarchical format for description of graphics representables that can be augmented by physics information and relational properties. It was developed for high energy physics event display applications and is especially suited to client/server or component frameworks. The GLAST experiment, an international effort led by NASA for a gamma-ray telescope to launch in 2006, chose HepRep to provide a flexible, extensible and maintainable framework for their event display without tying their users to any one graphics application. To support HepRep in their GUADI infrastructure, GLAST developed a HepRep filler and builder architecture. The architecture hides the details of XML and CORBA in a set of base and helper classes allowing physics experts to focus on what data they want to represent. GLAST has two GAUDI services: HepRepSvc, which registers HepRep fillers in a global registry and allows the HepRep to be exported to XML, and CorbaSvc, which allows the HepRep to be published through a CORBA interface and which allows the client application to feed commands back to GAUDI (such as start next event, or run some GAUDI algorithm). GLAST's HepRep solution gives users a choice of client applications, WIRED (written in Java) or FRED (written in C++ and Ruby), and leaves them free to move to any future HepRep-compliant event display.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 9 pages pdf, 15 figures. PSN THLT00

Approximation and Filtering Techniques for Navigation Data in Time-critical Electronic Warfare Systems

This paper presents a holistic solution to the navigation requirements in a time critical electronic warfare systems like missile warning systems (MWS). In a passive MWS using IR sensors the efficiency of the system is determined by attributes such as low false alarm rate, minimal response time and ability to track different IR radiating objects by association and correlation of consecutive detections through time. Such a system is required to be supported by a navigation system capable of accurate estimation of the aircraft position, attitude angles and altitude. In this paper, estimation techniques used to accurately calculate aircraft navigation data at the time of capture of IR frames are discussed. The paper discusses about synchronization of INGPS, IR sensors & Processor on to same timeline. The paper also intends to evaluate the performance of wavelet transform filter in effective elimination of noise in navigation parameters like acceleration and attitude angle rates for a better estimation of position and attitude.Defence Science Journal, 2013, 63(2), pp.204-209, DOI:http://dx.doi.org/10.14429/dsj.63.426

UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking

In recent years, numerous effective multi-object tracking (MOT) methods are developed because of the wide range of applications. Existing performance evaluations of MOT methods usually separate the object tracking step from the object detection step by using the same fixed object detection results for comparisons. In this work, we perform a comprehensive quantitative study on the effects of object detection accuracy to the overall MOT performance, using the new large-scale University at Albany DETection and tRACking (UA-DETRAC) benchmark dataset. The UA-DETRAC benchmark dataset consists of 100 challenging video sequences captured from real-world traffic scenes (over 140,000 frames with rich annotations, including occlusion, weather, vehicle category, truncation, and vehicle bounding boxes) for object detection, object tracking and MOT system. We evaluate complete MOT systems constructed from combinations of state-of-the-art object detection and object tracking methods. Our analysis shows the complex effects of object detection accuracy on MOT system performance. Based on these observations, we propose new evaluation tools and metrics for MOT systems that consider both object detection and object tracking for comprehensive analysis.Comment: 18 pages, 11 figures, accepted by CVI