Novel statistical modeling methods for traffic video analysis

Video analysis is an active and rapidly expanding research area in computer vision and artificial intelligence due to its broad applications in modern society. Many methods have been proposed to analyze the videos, but many challenging factors remain untackled. In this dissertation, four statistical modeling methods are proposed to address some challenging traffic video analysis problems under adverse illumination and weather conditions. First, a new foreground detection method is presented to detect the foreground objects in videos. A novel Global Foreground Modeling (GFM) method, which estimates a global probability density function for the foreground and applies the Bayes decision rule for model selection, is proposed to model the foreground globally. A Local Background Modeling (LBM) method is applied by choosing the most significant Gaussian density in the Gaussian mixture model to model the background locally for each pixel. In addition, to mitigate the correlation effects of the Red, Green, and Blue (RGB) color space on the independence assumption among the color component images, some other color spaces are investigated for feature extraction. To further enhance the discriminatory power of the input feature vector, the horizontal and vertical Haar wavelet features and the temporal information are integrated into the color features to define a new 12-dimensional feature vector space. Finally, the Bayes classifier is applied for the classification of the foreground and the background pixels. Second, a novel moving cast shadow detection method is presented to detect and remove the cast shadows from the foreground. Specifically, a set of new chromatic criteria is presented to detect the candidate shadow pixels in the Hue, Saturation, and Value (HSV) color space. A new shadow region detection method is then proposed to cluster the candidate shadow pixels into shadow regions. A statistical shadow model, which uses a single Gaussian distribution to model the shadow class, is presented to classify shadow pixels. Additionally, an aggregated shadow detection strategy is presented to integrate the shadow detection results and remove the shadows from the foreground. Third, a novel statistical modeling method is presented to solve the automated road recognition problem for the Region of Interest (RoI) detection in traffic video analysis. A temporal feature guided statistical modeling method is proposed for road modeling. Additionally, a model pruning strategy is applied to estimate the road model. Then, a new road region detection method is presented to detect the road regions in the video. The method applies discriminant functions to classify each pixel in the estimated background image into a road class or a non-road class, respectively. The proposed method provides an intra-cognitive communication mode between the RoI selection and video analysis systems. Fourth, a novel anomalous driving detection method in videos, which can detect unsafe anomalous driving behaviors is introduced. A new Multiple Object Tracking (MOT) method is proposed to extract the velocities and trajectories of moving foreground objects in video. The new MOT method is a motion-based tracking method, which integrates the temporal and spatial features. Then, a novel Gaussian Local Velocity (GLV) modeling method is presented to model the normal moving behavior in traffic videos. The GLV model is built for every location in the video frame, and updated online. Finally, a discriminant function is proposed to detect anomalous driving behaviors. To assess the feasibility of the proposed statistical modeling methods, several popular public video datasets, as well as the real traffic videos from the New Jersey Department of Transportation (NJDOT) are applied. The experimental results show the effectiveness and feasibility of the proposed methods

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

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

Robust 3D Action Recognition through Sampling Local Appearances and Global Distributions

3D action recognition has broad applications in human-computer interaction and intelligent surveillance. However, recognizing similar actions remains challenging since previous literature fails to capture motion and shape cues effectively from noisy depth data. In this paper, we propose a novel two-layer Bag-of-Visual-Words (BoVW) model, which suppresses the noise disturbances and jointly encodes both motion and shape cues. First, background clutter is removed by a background modeling method that is designed for depth data. Then, motion and shape cues are jointly used to generate robust and distinctive spatial-temporal interest points (STIPs): motion-based STIPs and shape-based STIPs. In the first layer of our model, a multi-scale 3D local steering kernel (M3DLSK) descriptor is proposed to describe local appearances of cuboids around motion-based STIPs. In the second layer, a spatial-temporal vector (STV) descriptor is proposed to describe the spatial-temporal distributions of shape-based STIPs. Using the Bag-of-Visual-Words (BoVW) model, motion and shape cues are combined to form a fused action representation. Our model performs favorably compared with common STIP detection and description methods. Thorough experiments verify that our model is effective in distinguishing similar actions and robust to background clutter, partial occlusions and pepper noise

Foreground Detection in Camouflaged Scenes

Foreground detection has been widely studied for decades due to its importance in many practical applications. Most of the existing methods assume foreground and background show visually distinct characteristics and thus the foreground can be detected once a good background model is obtained. However, there are many situations where this is not the case. Of particular interest in video surveillance is the camouflage case. For example, an active attacker camouflages by intentionally wearing clothes that are visually similar to the background. In such cases, even given a decent background model, it is not trivial to detect foreground objects. This paper proposes a texture guided weighted voting (TGWV) method which can efficiently detect foreground objects in camouflaged scenes. The proposed method employs the stationary wavelet transform to decompose the image into frequency bands. We show that the small and hardly noticeable differences between foreground and background in the image domain can be effectively captured in certain wavelet frequency bands. To make the final foreground decision, a weighted voting scheme is developed based on intensity and texture of all the wavelet bands with weights carefully designed. Experimental results demonstrate that the proposed method achieves superior performance compared to the current state-of-the-art results.Comment: IEEE International Conference on Image Processing, 201

A spatially distributed model for foreground segmentation

Foreground segmentation is a fundamental first processing stage for vision systems which monitor real-world activity. In this paper we consider the problem of achieving robust segmentation in scenes where the appearance of the background varies unpredictably over time. Variations may be caused by processes such as moving water, or foliage moved by wind, and typically degrade the performance of standard per-pixel background models. Our proposed approach addresses this problem by modeling homogeneous regions of scene pixels as an adaptive mixture of Gaussians in color and space. Model components are used to represent both the scene background and moving foreground objects. Newly observed pixel values are probabilistically classified, such that the spatial variance of the model components supports correct classification even when the background appearance is significantly distorted. We evaluate our method over several challenging video sequences, and compare our results with both per-pixel and Markov Random Field based models. Our results show the effectiveness of our approach in reducing incorrect classifications