Object (computer science)

About: Object (computer science) is a research topic. Over the lifetime, 106024 publications have been published within this topic receiving 1360115 citations. The topic is also known as: obj & Rq.

Learning to share visual appearance for multiclass object detection

Abstract: We present a hierarchical classification model that allows rare objects to borrow statistical strength from related objects that have many training examples. Unlike many of the existing object detection and recognition systems that treat different classes as unrelated entities, our model learns both a hierarchy for sharing visual appearance across 200 object categories and hierarchical parameters. Our experimental results on the challenging object localization and detection task demonstrate that the proposed model substantially improves the accuracy of the standard single object detectors that ignore hierarchical structure altogether.

Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles

Abstract: We present a novel approach for multi-object tracking which considers object detection and spacetime trajectory estimation as a coupled optimization problem. Our approach is formulated in a minimum description length hypothesis selection framework, which allows our system to recover from mismatches and temporarily lost tracks. Building upon a state-of-the-art object detector, it performs multiview/multicategory object recognition to detect cars and pedestrians in the input images. The 2D object detections are checked for their consistency with (automatically estimated) scene geometry and are converted to 3D observations which are accumulated in a world coordinate frame. A subsequent trajectory estimation module analyzes the resulting 3D observations to find physically plausible spacetime trajectories. Tracking is achieved by performing model selection after every frame. At each time instant, our approach searches for the globally optimal set of spacetime trajectories which provides the best explanation for the current image and for all evidence collected so far while satisfying the constraints that no two objects may occupy the same physical space nor explain the same image pixels at any point in time. Successful trajectory hypotheses are then fed back to guide object detection in future frames. The optimization procedure is kept efficient through incremental computation and conservative hypothesis pruning. We evaluate our approach on several challenging video sequences and demonstrate its performance on both a surveillance-type scenario and a scenario where the input videos are taken from inside a moving vehicle passing through crowded city areas.

Object tracking system with non-contact object detection and identification

Abstract: An object tracking system is provided for tracking the removal of objects from a location and the replacement of the objects at the location. The system includes a radio frequency identification (RFID) tag attached to each of the objects to be tracked and each tag has an antenna. When activated, the RFID tag of an object transmits a unique code identifying the object. A storage unit is provided at the location and the storage unit has a plurality of receptacles configured to receive objects replaced at the location. Each receptacle has an associated antenna for activating the RFID tag of an object in the receptacle and receiving the radio frequency transmitted code of the object. The antennae of the system can be capacitive plates for conveying the radio frequency transmissions through capacitive coupling or inductive loops for conveying the transmissions through inductive coupling. A computer-based controller is coupled to the antenna of the receptacles for receiving transmitted codes and determining based thereon the absence or presence and location of objects within the storage unit.

Discriminators for use in flow-based classification

Abstract: Any assessment of classification techniques requires data. This document describes sets of data intended to aid in the assessment of classification work. A number of data sets are described; each data set consists a number of objects, and each object is described by a group of features (also referred to as discriminators). Leveraged by a quantity of hand-classified data, each object within each data set represents a single flow of TCP packets between client and server. The features for each object consist of the (application-centric) classification derived elsewhere and a number of features derived as input to probabilistic classification techniques. In addition to describing the features, we also provide information allowing interested parties to retrieve these data sets for use in their own work. The data sets contain no site-identifying information; each object is only described by a set of statistics and a class that defines the causal application.

The Object Primer: Agile Model-Driven Development with UML 2.0

Abstract: Scott Ambler, award-winning author of Building Object Applications that Work, Process Patterns, and More Process Patterns, has revised his acclaimed first book, The Object Primer. Long prized by both students and professionals as the best introduction to object-oriented technology, this book is now completely up-to-date, with all modeling notation rewritten in the just-released UML 2.0. All chapters have been revised to take advantage of Agile Modeling (AM), which is presented in the new chapter 2 along with other important new modeling techniques. Review questions at the end of each chapter allow readers to test their newly acquired knowledge. In addition, Ambler takes time to reflect on the lessons learned over the past few years by discussing the proven benefits and drawbacks of the technology. This is the perfect book for any software development professional or student seeking an introduction to the concepts and terminology of object technology. Previous Edition Pb (2001): 0-521-78519-7 Scott W. Ambler is a senior object consultant with Ronin International, Inc. and a popular speaker at conferences worldwide. He has worked with OO technology since 1990 as a business architect, system analyst, system designer, mentor, Smalltalk/C++/Java developer, and OO software process manager. He is a member of the IEEE Computer Society, the ACM, and Mensa.