A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects

Recently, Minimum Cost Multicut Formulations have been proposed and proven to be successful in both motion trajectory segmentation and multi-target tracking scenarios. Both tasks benefit from decomposing a graphical model into an optimal number of connected components based on attractive and repulsive pairwise terms. The two tasks are formulated on different levels of granularity and, accordingly, leverage mostly local information for motion segmentation and mostly high-level information for multi-target tracking. In this paper we argue that point trajectories and their local relationships can contribute to the high-level task of multi-target tracking and also argue that high-level cues from object detection and tracking are helpful to solve motion segmentation. We propose a joint graphical model for point trajectories and object detections whose Multicuts are solutions to motion segmentation {\it and} multi-target tracking problems at once. Results on the FBMS59 motion segmentation benchmark as well as on pedestrian tracking sequences from the 2D MOT 2015 benchmark demonstrate the promise of this joint approach

High-dynamic-range imaging optical detectors

Imaging spectrometers allowing spatially resolved targets to be spectrally discriminated are valuable for remote sensing and defense applications. The drawback of such instruments is the need to quickly process very large amounts of data. In this paper we demonstrate two imaging systems which detect a dim target in a bright background, using the coherence contrast between them, generating much less data but only operating over a limited optical bandwidth. Both systems use a passband filter, a Michelson interferometer, coupling optics and a CCD camera. The first uses the interferometer in a spatial mode, by tilting one of the mirrors to create a set of line fringes on the CCD array. The visibility of these fringes is proportional to the degree of coherence. The interferogram is displayed spatially on the CCD array, as a function of the path differences. The second system uses the interferometer in a temporal mode. A coherent point target and an extended background are imaged through the interferometer onto the CCD array, and one of the interferometer's mirrors is scanned longitudinally to vary the path difference in time. In both cases the coherent target is detected over a large dynamic range down to negative signal-to-background power ratios (in dB). The paper describes an averaging technique to improve the signal-to-noise ratio and correction techniques required to extract interferograms from the images. The spatial technique developed has the advantage of using no moving parts

An Intelligent Monitoring System of Vehicles on Highway Traffic

Vehicle speed monitoring and management of highways is the critical problem of the road in this modern age of growing technology and population. A poor management results in frequent traffic jam, traffic rules violation and fatal road accidents. Using traditional techniques of RADAR, LIDAR and LASAR to address this problem is time-consuming, expensive and tedious. This paper presents an efficient framework to produce a simple, cost efficient and intelligent system for vehicle speed monitoring. The proposed method uses an HD (High Definition) camera mounted on the road side either on a pole or on a traffic signal for recording video frames. On the basis of these frames, a vehicle can be tracked by using radius growing method, and its speed can be calculated by calculating vehicle mask and its displacement in consecutive frames. The method uses pattern recognition, digital image processing and mathematical techniques for vehicle detection, tracking and speed calculation. The validity of the proposed model is proved by testing it on different highways.Comment: 5 page

A practical multirobot localization system

We present a fast and precise vision-based software intended for multiple robot localization. The core component of the software is a novel and efficient algorithm for black and white pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost cameras, the core algorithm is able to process hundreds of images per second while tracking hundreds of objects with a millimeter precision. In addition, we present the method's mathematical model, which allows to estimate the expected localization precision, area of coverage, and processing speed from the camera's intrinsic parameters and hardware's processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions is verified in several experiments. Apart from the method description, we also make its source code public at \emph{http://purl.org/robotics/whycon}; so, it can be used as an enabling technology for various mobile robotic problems