Objects In Complex Environments Research Articles

Detection of objects in complex environments by echolocating big brown bats

In previous psychophysical experiments, big brown bats detected or discriminated objects (monopole and dipole targets comprised of 15 mm diameter cylinders) presented standing on smooth surfaces with little clutter. Performance was determined by mutual masking of echoes from the targets themselves. In new experiments, bats did two-alternative (left/right) forced choice tasks to detect a two-cylinder dipole target in complex, cluttered environments. In experiment 1, the targets were set inside 14-mm-deep, 20-mm-diam holes in a layer of foam 25 mm thick. Cylinders of different heights protruded 13, 5, or 2 mm above the surface of the foam, or were recessed 1 mm below the surface. The bats performance varied as a function of protruding height. In experiment 2, the dipole target was embedded within arrays of distractor objects that varied in shape, size, orientation, and material. Both experiments explore how bats isolate the echoes from the dipole target from the mixture of echoes returning from the clutter—whether distractor objects or echoes from the sides of the holes. Big brown bats must engage in this acoustic scene analysis when catching insects, such as beetles, against a backdrop of foliage.

People tracking in surveillance applications

This paper presents a real-time algorithm that allows robust tracking of multiple objects in complex environments. Foreground pixels are detected using luminance contrast and grouped into blobs. Blobs from two consecutive frames are matched creating the matching matrices. Tracking is performed using direct and inverse matching matrices. This method successfully solves blobs merging and splitting. Some application in automatic surveillance systems are suggested by linking trajectories and blob position information with the events to be detected.

Advanced Surveillance: From Tracking To Event Detection

The authors suggest an automatic surveillance system based on a robust and simple algorithm for foreground detection and tracking of multiple objects in complex environments. It is possible to obtain enough information from the characteristics of the detected objects, their centroid's position and their trajectories as to detect some predefined events. Working without the operator supervision the application analyses the video streams from the CCTV surveillance system, discarding empty scenes for monitoring or recording, showing to the operator those fulfilling predefined requirements, like crowding, and rising an alarm to call the operator attention when it decides that an event may be taking place. The application is based on very simple algorithms in order to allow a single computer the analysis of up to eight video streams in real-time. Blob and scene characteristics are used to create a low-level description of predefined events. The application detects events by comparing the sequence's parameters with those associated with the events for the current scenario. The provided examples are tailored to the specific demands of a surveillance system in a public transport environment.

Crowded field 3D spectroscopy

AbstractThe quantitative spectroscopy of stellar objects in complex environments is mainly limited by the ability of separating the object from the background. Standard slit spectroscopy, restricting the field of view to one dimension, is obviously not the proper technique in general. The emerging Integral Field (3D) technique with spatially resolved spectra of a two‐dimensional field of view provides a great potential for applying advanced subtraction methods. In this paper an image reconstruction algorithm to separate point sources and a smooth background is applied to 3D data. Several performance tests demonstrate the photometric quality of the method. The algorithm is applied to real 3D observations of a sample Planetary Nebula in M31, whose spectrum is contaminated by the bright and complex galaxy background. The ability of separating sources is also studied in a crowded field in M33. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)