Reliable Recognition of Partially Occluded Objects with Correlation Filters

Most of captured images present degradations due to blurring and additive noise; moreover objects of interest can be geometrically distorted. The classical methods for pattern recognition based on correlation are very sensitive to intensity degradations and geometric distortions. In this work, we propose an adaptive generalized filter based on synthetic discriminant function (SDF). With the help of computer simulation we analyze and compare the performance of the adaptive correlation filter with that of common correlation filters in terms of discrimination capability and accuracy of target location when input scenes are degraded and a target is geometrically distorted.

Modeling Self-Occlusions/Disocclusions in Dynamic Shape and Appearance Tracking for Obtaining Precise Shape We present a method to determine the precise shape of a dynamic object from video. This problem is fundamental to computer vision, and has a number of applications, for example, 3D video/cinema post-production, activity recognition and augmented reality. Current tracking algorithms that determine precise shape can be roughly divided into two categories: 1) Global statistics partitioning methods, where the shape of the object is determined by discriminating global image statistics, and 2) Joint shape and appearance matching methods, where a template of the object from the previous frame is matched to the next image. The former is limited in cases of complex object appearance and cluttered background, where global statistics cannot distinguish between the object and background. The latter is able to cope with complex appearance and a cluttered background, but is limited in cases of camera viewpoint change and object articulation, which induce self-occlusions and self-disocclusions of the object of interest. The purpose of this thesis is to model self-occlusion/disocclusion phenomena in a joint shape and appearance tracking framework. We derive a non-linear dynamic model of the object shape and appearance taking into account occlusion phenomena, which is then used to infer self-occlusions/disocclusions, shape and appearance of the object in a variational optimization framework. To ensure robustness to other unmodeled phenomena 5 that are present in real-video sequences, the Kalman filter is used for appearance updating. Experiments show that our method, which incorporates the modeling of self-occlusion/disocclusion, increases the accuracy of shape estimation in situations of viewpoint change and articulation, and out-performs current state-of-the-art methods for shape tracking.

Visual object tracking is a significant technology for camera-based sensor networks applications. Multilayer convolutional features comprehensively used in correlation filter (CF)-based tracking algorithms have achieved excellent performance. However, there are tracking failures in some challenging situations because ordinary features are not able to well represent the object appearance variations and the correlation filters are updated irrationally. In this paper, we propose a local–global multiple correlation filters (LGCF) tracking algorithm for edge computing systems capturing moving targets, such as vehicles and pedestrians. First, we construct a global correlation filter model with deep convolutional features, and choose horizontal or vertical division according to the aspect ratio to build two local filters with hand-crafted features. Then, we propose a local–global collaborative strategy to exchange information between local and global correlation filters. This strategy can avoid the wrong learning of the object appearance model. Finally, we propose a time-space peak to sidelobe ratio (TSPSR) to evaluate the stability of the current CF. When the estimated results of the current CF are not reliable, the Kalman filter redetection (KFR) model would be enabled to recapture the object. The experimental results show that our presented algorithm achieves better performances on OTB-2013 and OTB-2015 compared with the other latest 12 tracking algorithms. Moreover, our algorithm handles various challenges in object tracking well.

This paper presents efficient object tracking in video sequences using multiple features by embedding mean shift into particle filters. When clutter background and occlusions are present. Particle filtering is used because it is very robust and performs well for non-linear and non-Gaussian dynamic state estimation problems. The image features, such as shape, texture, color, contours, and random motion appearance can be used to track the moving object(s) in videos. We proposed a smart video surveillance system with real-time as well as offline stored video database for moving object detection, classification and tracking capabilities. The graphical user interface of the proposed system using MATLAB 2012b is implemented that operates on both color and gray scale video images from a stationary camera. In this method, we proposed real time moving object detection and tracking system using static webcam that can processes 800*600, 640*480, and 320*240 resolution video sequences for capturing live scene as well as stored database of segmented videos. Issues related with object detection and tracking is jointly employed using mean shift and particle filters. Tracker will estimate the dynamic shape and random appearance of objects. Tracking requires location and shape of object in every segmented frame. Rectangular bounding box is used to improve the estimate of its shape and appearance. Target object may experience partial occlusions, intersection with other objects with similar color distributions, abrupt motion speed changes, and cluttered background. Test result shows improvement in terms of robustness, tracking drifts, accuracy of tracked bounding box to occlusions.

3D target recognition is of significant interest because representing the object in 3D space couuld essentially provide a solution to pose variation and self-occlusion problems that are big challenges in 2D pattern recognition. Correlation filers have been used in a variety of 2D pattern matching applications and many correlation filter designs have been developed to handle problems such as rotations. Correlation filters also offer other benefits such as shift-invariance, graceful degradation and closed-form solutions. The 3D extension of correlation filter is a natural extension to handle 3D pattern recognition problem. In this paper, we propose a 3D correlation filter design method based on cylindrical circular harmonic function (CCHF) and use LADAR imagery to illustrate the good performance of CCHF filters.

Tensor correlation filter based class-dependence feature analysis for face recognition

The paper summarizes new research in performing face recognition using advanced correlation filters. We examine the performance of such filters in the area of biometrics for face authentication. We also compare results when the filters are applied to face identification. Our results are based on the illumination subsets of the CMU PIE database. We also present methods that reduce the memory requirements of these filters to run on limited computational resources, including computationally efficient methods of synthesizing these filters. Finally, we describe an online training algorithm implemented on a face verification system for synthesizing correlation filters from a video stream to handle pose/scale variations. The system also uses an efficient scheme to perform face localization within the current framework during the authentication stage.

Adaptive spatial-temporal surrounding-aware correlation filter tracking via ensemble learning

A correlation filter for reliable recognition of a target embedded into a cluttered background blurred by relative motion between a camera and an input scene is derived. With a help of computer simulations the performance of the proposed filter in terms of discrimination capability and location errors is analyzed.

Object tracking in satellite videos faces various challenges such as target occlusion, target rotation, and background clutter. This study proposes a correlation particle filter algorithm with motion estimation for object tracking in satellite videos. The tracker, called CPKF, combines the strengths of the correlation, particle, and Kalman filters. Compared with existing tracking methods based on correlation filters, the proposed tracker has three major advantages: (1) Particle sampling, and motion estimation build robustness against partial and complete occlusion. (2) Color histogram model makes it robust to target rotation. (3) Fusion of multiple feature response maps effectively handle background clutter and low contrast. The experimental results demonstrate that the proposed tracking algorithm performs better than state-of-the-art methods.

Real-time tracking of multiple objects using adaptive correlation filters with complex constraints

In recent years, the correlation filter based algorithms show impressive performance for visual tracking. However, the object representations (i.e., feature descriptors) are still not robust. In addition, the models in existing correlation filter based algorithms may be updated by using corrupted samples when the tracking targets are occluded, thus leading to the drifting problem. In this paper, we present a weighted foreground appearance feature descriptor which effectively characterizes the appearance of objects. Moreover, we propose an adaptive model updating strategy to mitigate the problem that the models are updated by using corrupted samples. Our works are based on a recently proposed correlation filter based algorithm, i.e., Staple. By effectively combining the proposed feature descriptor with the adaptively updated Staple framework, the proposed algorithm is highly robust and it can achieve promising performance under complex conditions, such as deformation, rotation and scale variation. Experimental results on the OTB-50 and OTB-100 datasets demonstrate the effectiveness of the proposed tracking algorithm, compared with several other state-of-the-art algorithms.

Designing a pattern classifier remains a difficult problem especially in the presence of noise and other degradations. Combination of multiple classifiers appears to be a good way of retaining the strengths of different classifiers while avoiding their weaknesses. Different combination schemes were proposed in the literature. As a special case of combining multiple classifiers, we consider combining correlators. Correlators are attractive for use in Automatic Target Recognition systems. Many correlation filter designs have been developed, each with its own features. Some filter designs maximize noise tolerance but do not provide sharp peaks. On the other hand, some correlation filters yield sharp correlation peaks but are overly sensitive to input noise. In this research effort, we explore the use of artificial neural network as a tool for combining correlators. Results of this implementation show improvements and indicate that combination of multiple correlators can potentially improve the classification performance.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimization

Optical pattern recognition in defocused images using correlation filters