1,085 research outputs found

    Vehicle Detection and Tracking Techniques: A Concise Review

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    Vehicle detection and tracking applications play an important role for civilian and military applications such as in highway traffic surveillance control, management and urban traffic planning. Vehicle detection process on road are used for vehicle tracking, counts, average speed of each individual vehicle, traffic analysis and vehicle categorizing objectives and may be implemented under different environments changes. In this review, we present a concise overview of image processing methods and analysis tools which used in building these previous mentioned applications that involved developing traffic surveillance systems. More precisely and in contrast with other reviews, we classified the processing methods under three categories for more clarification to explain the traffic systems

    MOVING OBJECT DETECTION USING BIT PLANE SLICING

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    This thesis presents moving object detection algorithm using bit plane extraction of successive frames and comparing the respective bit planes by XOR operation. The proposed methodworks on 8-bit grayscale video frames obtained from a static camera. This algorithm is able to detect the motion of single and multiple objects in outside and inside environments. Algorithm has been implemented in MATLAB by using several videos from VISOR database and was compared to existing conventional methods to show its effectiveness. Performance of an algorithm was evaluated based on ground truth metrics and results in terms of sensitivity, specificity, positive prediction and accuracy proved the validity of it. Results show that the proposed algorithm performs better in terms of mentioned metrics in comparison to other algorithms.

    Airborne photogrammetry and LIDAR for DSM extraction and 3D change detection over an urban area : a comparative study

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    A digital surface model (DSM) extracted from stereoscopic aerial images, acquired in March 2000, is compared with a DSM derived from airborne light detection and ranging (lidar) data collected in July 2009. Three densely built-up study areas in the city centre of Ghent, Belgium, are selected, each covering approximately 0.4 km(2). The surface models, generated from the two different 3D acquisition methods, are compared qualitatively and quantitatively as to what extent they are suitable in modelling an urban environment, in particular for the 3D reconstruction of buildings. Then the data sets, which are acquired at two different epochs t(1) and t(2), are investigated as to what extent 3D (building) changes can be detected and modelled over the time interval. A difference model, generated by pixel-wise subtracting of both DSMs, indicates changes in elevation. Filters are proposed to differentiate 'real' building changes from false alarms provoked by model noise, outliers, vegetation, etc. A final 3D building change model maps all destructed and newly constructed buildings within the time interval t(2) - t(1). Based on the change model, the surface and volume of the building changes can be quantified

    A Fusion Framework for Camouflaged Moving Foreground Detection in the Wavelet Domain

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    Detecting camouflaged moving foreground objects has been known to be difficult due to the similarity between the foreground objects and the background. Conventional methods cannot distinguish the foreground from background due to the small differences between them and thus suffer from under-detection of the camouflaged foreground objects. In this paper, we present a fusion framework to address this problem in the wavelet domain. We first show that the small differences in the image domain can be highlighted in certain wavelet bands. Then the likelihood of each wavelet coefficient being foreground is estimated by formulating foreground and background models for each wavelet band. The proposed framework effectively aggregates the likelihoods from different wavelet bands based on the characteristics of the wavelet transform. Experimental results demonstrated that the proposed method significantly outperformed existing methods in detecting camouflaged foreground objects. Specifically, the average F-measure for the proposed algorithm was 0.87, compared to 0.71 to 0.8 for the other state-of-the-art methods.Comment: 13 pages, accepted by IEEE TI

    PERFORMANCE METRICS IN VIDEO SURVEILLANCE SYSTEM

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    Video surveillance is an active research topic in computer vision. One of the areas that are being actively researched is on the abilities of surveillance systems to track multiple objects over time in occluded scenes and to keep a consistent identity for each target object. These abilities enable a surveillance system to provide crucial information about moving objects behaviour and interaction. This survey reviews the recent developments in moving object detection and also different techniques and approaches in multiple objects tracking that have been developed by researchers. The algorithms and filters that can be incorporated in tracking multiples object to solve the occluded and natural busy scenes in surveillance systems are also reviewed in this paper. This survey is meant to provide researchers in the field with a summary of progress achieved up to date in multiple moving objects tracking. Despite recent progress in computer vision and other related areas, there are still major technical challenges that need to be solved before reliable automated video surveillance system can be realized

    Real-time On-board Object Tracking for Cooperative Flight Control

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    One of possible cooperative Situations for flights could be a scenario when the decision on a new path is taken by A Certain fleet member, who is called the leader. The update on the new path is Transmitted to the fleet members via communication That can be noisy. An optical sensor can be used as a back-up for re-estimating the path parameters based on visual information. For A Certain topology, the issue can be solved by continuous tracking of the leader of the fleet in the video sequence and re-adjusting parameters of the flight, accordingly. To solve such a problem of a real time system has been developed for Recognizing and tracking 3D objects. Any change in the 3D position of the leading object is Determined by the on-board system and adjustments of the speed, pitch, yaw and roll angles are made to sustain the topology. Given a 2D image acquired by an on-board camera, the system has to perform the background subtraction, recognize the object, track it and evaluate the relative rotation, scale and translation of the object. In this paper, a comparative study of different algorithms is Carried out based on time and accuracy constraints. The solution for 3D pose estimation is provided based on the system of invariant Zernike moments. The candidate techniques solving the complete set of procedures have been Implemented on Texas Instruments TMS320DM642 EVM board. It is shown That 14 frames per second can be processed; That supports the real time Implementation of the tracking system with the reasonable accuracy
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