Aishy Amer

Voting-based simultaneous tracking of multiple video objects

This paper proposes an automatic object tracking method based on both object segmentation and motion estimation for real-time content-oriented video applications. The method focuses on the issues of speed of execution and reliability in the presence of noise, coding artifacts, shadows, occlusion, and object split. Objects are tracked based on the similarity of their features in successive frames. This is done in three steps: feature extraction, object matching, and feature monitoring. In the first step, objects are segmented and their spatial and temporal features are computed. In the second step, using a nonlinear two-stage voting strategy, each object of the previous frame is matched with an object of the current frame creating a unique correspondence. In the third step, object changes, such objects occlusion or split, are monitored and object features are corrected. These new features are then used to update results of previous steps creating module interaction. The contributions in this paper are the real-time two-stage voting strategy, the monitoring of object changes to handle occlusion and object split, and the spatiotemporal adaptation of the tracking parameters. Experiments on indoor and outdoor video shots containing over 6000 frames, including deformable objects, multi-object occlusion, noise, and coding and object segmentation artifacts have demonstrated the reliability and real-time response of the proposed method.

Reliable and fast structure-oriented video noise estimation

The purpose of this paper is to introduce a fast automated white-noise estimation method which gives reliable estimates in images with smooth and textured areas. This method is a block-based method that takes the image structure into account and uses a measure other than the variance to determine if a block is homogeneous. It uses no thresholds and automates the way that block-based methods stop the averaging of block variances. The proposed method selects intensity-homogeneous blocks in an image by rejecting blocks of structure using a new structure analyzer. The analyzer used is based on high-pass operators and special masks for comers to allow implicit detection of structure and to stabilize the homogeneity estimation. For a typical image quality (PSNR of 20-40 dB) the proposed method outperforms other methods significantly and the worst-case estimation error is 3 dB which is suitable for real applications such as video surveillance or broadcasts. The method performs well even in images with few smooth areas and in highly noisy images.

Editorial: Introduction to the special issue on video object processing for surveillance applications

Video-based surveillance (or video surveillance) is one of the fastest growing sectors in the security market. This is due to the high amount of useful information that can be extracted from a video sequence. In particular, the automatic real-time processing of video objects, i.e., the extraction of video objects and related high-level content, is hereby of paramount importance. High-level video content, e.g., object activities and events, are generally related to the movement of video objects. This is related to the human visual system (HVS) which is strongly attracted to moving objects creating luminance change [Nothdurft (1993); Franconeri and Simons (2003); Abrams and Christ (2003)].

A Real-Time Adaptive Thresholding for Video Change Detection

A real-time adaptive non-parametric thresholding algorithm for change detection is proposed in this paper. Based on the estimation of the scatter of regions of change in a difference image, a threshold of each image block is computed discriminatively, then the global threshold is obtained by averaging all the thresholds for image blocks. The block threshold is calculated differently for regions of change and background. Experimental results show the proposed thresholding algorithm performs well for change detection with high efficiency.

Robust Global Motion Estimation Oriented to Video Object Segmentation

Most global motion estimation (GME) methods are oriented to video coding while video object segmentation methods either assume no global motion (GM) or directly adopt a coding-oriented method to compensate for GM. This paper proposes a hierarchical differential GME method oriented to video object segmentation. A scheme which combines three-step search and motion parameters prediction is proposed for initial estimation to increase efficiency. A robust estimator that uses object information to reject outliers introduced by local motion is also proposed. For the first frame, when the object information is unavailable, a robust estimator is proposed which rejects outliers by examining their distribution in local neighborhoods of the error between the current and the motion-compensated previous frame. Subjective and objective results show that the proposed method is more robust, more oriented to video object segmentation, and faster than the referenced methods.

Homogeneity Localization Using Particle Filters With Application to Noise Estimation

This paper proposes a method for localizing homogeneity and estimating additive white Gaussian noise (AWGN) variance in images. The proposed method uses spatially and sparsely scattered initial seeds and utilizes particle filtering techniques to guide their spatial movement towards homogeneous locations. This way, the proposed method avoids the need to perform the full search associated with block-based noise estimation methods. To achieve this, the paper proposes for the particle filter a dynamic model and a homogeneity observation model based on Laplacian structure detectors. The variance of AWGN is robustly estimated from the variances of blocks in the detected homogeneous areas. A proposed adaptive trimmed-mean based robust estimator is used to account for the reduction in estimation samples from the full search approach. Our results show that the proposed method reduces the number of homogeneity measurements required by block-based methods while achieving more accuracy.

A Real-Time Technique for Spatio–Temporal Video Noise Estimation

This paper proposes a spatio-temporal technique for estimating the noise variance in noisy video signals, where the noise is assumed to be additive white Gaussian noise. The proposed technique utilizes domain-wise (spatial, temporal, and spatio-temporal) video information independently for improved reliability. It divides the video signal into cubes and measures their homogeneity using Laplacian of Gaussian based operators. Then, the variances of homogeneous cubes are selected to estimate the noise variance. A least median of squares robust estimator is used to reject outliers and produce domain-wise noise variance estimates which are adaptively integrated to obtain the final frame-wise estimate. The proposed technique estimates the noise variance reliably in video sequences with both low and high video activities (e.g., fast motion or high spatial structure) and it produces a maximum estimation error of 1.7-dB peak signal-to-noise ratio. The proposed method is fast when compared to referenced methods.

A new video noise reduction algorithm using spatial subbands

Noise filtering in television receivers provides an attractive feature, especially under sub-optimal reception conditions. This paper describes a concept of temporal recursive noise filtering in video signals for a standard interlaced TV environment based on the visual noise perception characteristics and on different processing modules in the spatial subbands. The subband (highs and lows channels) oriented algorithm allows optimal noise filtering based on a 2-channel-model of the human visual system. An adequate combination of two different subband based motion detection techniques is proposed. Therefore, different procedures in the highs and lows channels lead to results well matched to the requirements of the human visual system. An evaluation of the new concept is included. A comparison to known motion adaptive noise filter techniques shows a significant improvement of the scheme. Experiments using the new noise reduction scheme on MPEG-received video images show that a good quality improvement of noisy source materials can be achieved.

A Modular Distributed Video Surveillance System Over IP

We present an automated and distributed real-time video surveillance system which can be used for the detection of objects and events in a wide range of applications. Video feeds are captured from multiple sources, processed and streamed over the Internet for viewing and analysis. Components of the system can be interconnected in several manners, thus forming flexible systems. The experimental results show a system that handles multiple video feeds, running on standard computers and yielding fluid video. Several interconnected clients can view multiple feeds simultaneously, as well as the event listing

Memory-based Spatio-Temporal Real-Time Object Segmentation for Video Surveillance

In real-time content-oriented video applications, fast unsupervised object segmentation is required. This paper proposes a real-time unsupervised object segmentation that is stable throughout large video shots. It trades precise segmentation at object boundaries for speed of execution and reliability in varying image conditions. This interpretation is most appropriate to applications such as surveillance and video retrieval where speed and temporal reliability are of more concern than accurate object boundaries. Both objective and subjective evaluations, and comparisons to other methods show the robustness of the proposed methods while being of reduced complexity. The proposed algorithm needs on average 0.15 seconds per image. The proposed segmentation consists of four steps: motion detection, morphological edge detection, contour analysis, and object labeling. The contributions in this paper are: a segmentation process of simple but effective tasks avoiding complex operations, a reliable memory-based noise-adaptive motion detection, and a memory-based contour tracing and analysis method. The proposed contour tracing aims 1) at finding contours with complex structure such as those containing dead or inner branches and 2) at spatial and temporal adaptive selection of contours. The motion detection is spatio-temporal adaptive as it uses estimated intra-image noise variance and detected inter-image motion.