Moving Shadow Detection in Video Using Cepstrum

Cataloged from PDF version of article.Moving shadows constitute problems in various applications such as image segmentation and object tracking. The main cause of these problems is the misclassification of the shadow pixels as target pixels. Therefore, the use of an accurate and reliable shadow detection method is essential to realize intelligent video processing applications. In this paper, a cepstrum-based method for moving shadow detection is presented. The proposed method is tested on outdoor and indoor video sequences using well-known benchmark test sets. To show the improvements over previous approaches, quantitative metrics are introduced and comparisons based on these metrics are made

Effective moving cast shadow detection for monocular color image sequences

For an accurate scene analysis in monocular image sequences, a robust segmentation of a moving object from the static background is generally required. However, the existence of moving cast shadow may lead to an inaccurate object segmentation, and as a result, lead to further erroneous scene analysis. An effective detection of moving cast shadow in monocular color image sequences is developed. Firstly, by realizing the various characteristics of shadow in luminance, chrominance, and gradient density, an indicator, called shadow confidence score, of the probability of the region classified as cast shadow is calculated. Secondly the canny edge detector is employed to detect edge pixels in the detected region. These pixels are then bounded by their convex hull, which estimates the position of the object. Lastly, by analyzing the shadow confidence score and the bounding hull, the cast shadow is identified as those regions outside the bounding hull and with high shadow confidence score. A number of typical outdoor scenes are evaluated and it is shown that our method can effectively detect the associated cast shadow from the object of interest.published_or_final_versio

3D Vehicle Extraction and Tracking from Multiple Viewpoints for Traffic Monitoring by using Probability Fusion Map

This paper presents a novel solution of vehicle occlusion and 3D measurement for traffic monitoring by data fusion from multiple stationary cameras. Comparing with single camera based conventional methods in traffic monitoring, our approach fuses video data from different viewpoints into a common probability fusion map (PFM) and extracts targets. The proposed PFM concept is efficient to handle and fuse data in order to estimate the probability of vehicle appearance, which is verified to be more reliable than single camera solution by real outdoor experiments. An AMF based shadowing modeling algorithm is also proposed in this paper in order to remove shadows on the road area and extract the proper vehicle regions

Cepstrum based method for moving shadow detection in video

Moving shadows constitute problems in various applications such as image segmentation and object tracking. Main cause of these problems is the misclassification of the shadow pixels as target pixels. Therefore, the use of an accurate and reliable shadow detection method is essential to realize intelligent video processing applications. In this paper, the cepstrum based method for moving shadow detection is presented. The proposed method is tested on outdoor and indoor video sequences using well-known benchmark test sets. To show the improvements over previous approaches, quantitative metrics are introduced and comparisons based on these metrics are made. © 2011 Springer Science+Business Media B.V

Shadow-aware object-based video processing

Local illumination changes due to shadows often reduce the quality of object-based video composition and mislead object recognition. This problem makes shadow detection a desirable tool for a wide range of applications, such as video production and visual surveillance. In this paper, an algorithm for the isolation of video objects from the local illumination changes they generate in real world sequences when camera, illumination and the scene characteristics are not known is presented. The algorithm combines a change detector and a shadow detector with a spatio-temporal verification stage. Colour information and spatio-temporal constraints are embedded to define the overall algorithm. Colour information is exploited in a selective way. First, relevant areas to analyse are identified in each image. Then, the colour components that carry most of the needed information are selected. Finally, spatial and temporal constraints are used to verify the results of the colour analysis. The proposed algorithm is demonstrated on both indoor and outdoor video sequences. Moreover, performance comparisons show that the proposed algorithm outperforms state-of-the-art methods

Spatio-Temporal Shadow Segmentation and Tracking

Shadow segmentation is a critical issue for systems aiming at extracting, tracking or recognizing objects in a given scene. Shadows can in fact modify the shape and colour of objects and therefore affect scene analysis and interpretation systems in many applications, such as video database search and retrieval, as well as video analysis in applications such as video surveillance. We present a shadow segmentation algorithm which includes two stages. The first stage extracts moving cast shadows in each frame of the sequence. The second stage tracks the extracted shadows in the subsequent frames. Tentative moving shadow regions are first identified based on spectral and geometrical properties of shadows. In order to confirm this tentative identification, shadow regions are then tracked over time. This second stage aims at exploiting the prior knowledge of a shadow detected in previous frames by evaluating its temporal behaviour. Shadow tracking is a difficult task, since colour, texture, and motion features in shadow regions cannot be used for solving the correspondence problem. Colour and texture change according to changes in the background's characteristics. The measurement of motion cannot be reliably computed for shadows. Therefore shadows may be described only by a limited amount of information. The proposed tracking algorithm makes use of this information and provides a reliability estimation of shadow recognition results of the first stage over time. This temporal analysis eliminates the possible ambiguities of the first stage and improves the efficiency of the overall shadow detection algorithm. The benefit of the proposed shadow segmentation and tracking algorithm is evaluated on both indoor and outdoor scenes. The obtained results are validated based on subjective as well as objective comparisons

Vision-based Detection, Tracking and Classification of Vehicles using Stable Features with Automatic Camera Calibration

A method is presented for segmenting and tracking vehicles on highways using a camera that is relatively low to the ground. At such low angles, 3D perspective effects cause significant appearance changes over time, as well as severe occlusions by vehicles in neighboring lanes. Traditional approaches to occlusion reasoning assume that the vehicles initially appear well-separated in the image, but in our sequences it is not uncommon for vehicles to enter the scene partially occluded and remain so throughout. By utilizing a 3D perspective mapping from the scene to the image, along with a plumb line projection, a subset of features is identified whose 3D coordinates can be accurately estimated. These features are then grouped to yield the number and locations of the vehicles, and standard feature tracking is used to maintain the locations of the vehicles over time. Additional features are then assigned to these groups and used to classify vehicles as cars or trucks. The technique uses a single grayscale camera beside the road, processes image frames incrementally, works in real time, and produces vehicle counts with over 90% accuracy on challenging sequences. Adverse weather conditions are handled by augmenting feature tracking with a boosted cascade vehicle detector (BCVD). To overcome the need of manual camera calibration, an algorithm is presented which uses BCVD to calibrate the camera automatically without relying on any scene-specific image features such as road lane markings