Consistent fish tracking via multiple underwater cameras

In this paper, we present a soft biometrics based appearance model for multi-target tracking in a single camera. Track lets, the short-term tracking results, are generated by linking detections in consecutive frames based on conservative constraints. Our goal is to "re-stitching" the adjacent track lets that contain the same target so that robust long-term tracking results can be achieved. As the appearance of the same target may change greatly due to heavy occlusion, pose variations and changing lighting conditions, a discriminative appearance model is crucial for association-based tracking. Unlike most previous methods which simply use the similarity of color histograms or other low level features to construct the appearance model, we propose to use the fusion of soft biometrics generated from sub-track lets to learn a discriminative appearance model in an online manner. Compared to low level features, soft biometrics are robust against appearance variation. The experimental results demonstrate that our method is robust and greatly improves the tracking performance over the state-of-the-art method.

We present an approach for online learning of discriminative appearance models for robust multi-target tracking in a crowded scene from a single camera. Although much progress has been made in developing methods for optimal data association, there has been comparatively less work on the appearance models, which are key elements for good performance. Many previous methods either use simple features such as color histograms, or focus on the discriminability between a target and the background which does not resolve ambiguities between the different targets. We propose an algorithm for learning a discriminative appearance model for different targets. Training samples are collected online from tracklets within a time sliding window based on some spatial-temporal constraints; this allows the models to adapt to target instances. Learning uses an Ad-aBoost algorithm that combines effective image descriptors and their corresponding similarity measurements. We term the learned models as OLDAMs. Our evaluations indicate that OLDAMs have significantly higher discrimination between different targets than conventional holistic color histograms, and when integrated into a hierarchical association framework, they help improve the tracking accuracy, particularly reducing the false alarms and identity switches.

Co-trained generative and discriminative trackers with cascade particle filter

Speeded Up Robust Features (SURF) algorithm is a fast robust local feature matching method. Because of the advantage of invariance of scale and rotation, the SURF algorithm has been widely used in the image registration. However, due to the acquired angle, imaging mode, different resolution, SURF algorithm becomes quite difficult in the image registration. This paper presents a normalized SURF algorithm which can reduce the impact of hue difference between remote sensing images. Combined with the idea of two-way matching, the normalized SURF is able to improve the accuracy of the image registration. The experimental results show the normalized SURF algorithm can keep greater accuracy and relatively higher speed of matching than the original SURF algorithm.

In this paper, we present a wide area surveillance system that detects, tracks and classifies moving objects across multiple cameras. At the single camera level, tracking is performed using a voting based approach that utilizes color and shape cues to establish correspondence. The system uses the single camera tracking results along with the relationship between camera field of view (FOV) boundaries to establish correspondence between views of the same object in multiple cameras. To this end, a novel approach is described to find the relationships between the FOV lines of cameras. The proposed approach combines tracking in cameras with overlapping and/or non-overlapping FOVs in a unified framework, without requiring explicit calibration. The proposed algorithm has been implemented in a real time system. The system uses a client-server architecture and runs at 10 Hz with three cameras.

An appearance model is critical for most modern trackers. While numerous novel appearance models have been proposed with demonstrated success, challenges such as occlusion and drifting are still not well addressed. In this paper, we propose a novel contextual bag-of-words (CBOW) discriminative appearance model that appropriately handles drifting and occlusion. Specifically, a contextual region containing both the target and its surroundings is explored to construct a compact representation with two bags-of-words. Each word carries discriminative appearance information that is learned by Bayesian inference. An adaptive updating approach, where the background BOWs of the CBOW model acts as a "sentinel" to prevent the integration of the background appearance with the object model, is introduced to alleviate the drifting problem. Based on CBOW, visual tracking is posed within a Bayesian framework. Moreover, an explicit detection method is employed to handle severe occlusions, which further reduces drifting. Two trackers based on the same CBOW model are implemented using either handcrafted color/texture or deep convolutional features. Our trackers are evaluated based on the popular OTB50 and VOT2015 benchmarks and perform competitively against the current state of the art. In addition, they outperform two recent BOWs trackers by a large margin using the currently available figures of merit. To take into account a tracking breakdown, we propose a new figure of merit called the mean maximum-tracked-frame ratio (MTFR) that evaluates a tracker's temporal persistence without any interruption. Experiments with OTB50 demonstrate the superior robustness of our tracker compared with all other evaluated trackers on the basis of MTFR.

위성 SAR 영상의 활용이 증가하면서 영상의 해석 및 융합을 위한 정밀 기하보정에 대한 필요성이 높아지고 있다. 특히 광역감시 목적으로 활용되기 위해 서로 다른 해상도를 갖는 SAR 영상간 정보융합도 활발해지고 있다. 일반적으로 SAR 영상의 기하보정은 위성의 궤도 및 자세정보를 활용하여 수행할 수 있지만 SAR 센서의 궤도 및 시스템 오차, 대상지형 특성에 의한 왜곡으로 인해 추가적인 보정이 필요하게 된다. SAR 영상을 통한 변화탐지나 타 영상과의 융합에 적용하기 위해서는 기하 오차 보정이 반드시 선행되어야 한다. 이를 위해 다수의 지상 기준점을 선정하고 이를 포함하는 기준 영상과 비교하여 원본 영상에서 대응점을 찾는 방식으로 정밀 기하보정을 수행할 수 있다. Speeded Up Robust Feature (SURF) 기법은 쉽고 빠르게 영상의 기준점을 찾을 수 있지만 상대적으로 해상도가 낮고 스펙클 잡음에 영향을 받는 SAR 영상에서는 활용하기가 어렵다고 알려져 있다. 본 논문에서는 SURF 기법을 위성 SAR 영상에 적용할 때 발생할 수 있는 오차를 추출하고 영상 특성에 따른 성능 변화를 분석하였다. SURF 알고리즘의 적용이 가능한 입력 변수의 적정 범위를 제시하고 그에 따른 영상 정합의 오차를 분석하여 중저해상도의 위성 SAR 영상에 대해서도 SURF 기법을 통한 기하 보정 및 영상 정합이 적용될 수 있음을 검증하였다. As applications of spaceborne SAR imagery are extended, there are increased demands for accurate registrations for better understanding and fusion of radar images. It becomes common to adopt multi-resolution SAR images to apply for wide area reconnaissance. Geometric correction of the SAR images can be performed by using satellite orbit and attitude information. However, the inherent errors of the SAR sensor's attitude and ground geographical data tend to cause geometric errors in the produced SAR image. These errors should be corrected when the SAR images are applied for multi-temporal analysis, change detection applications and image fusion with other sensor images. The undesirable ground registration errors can be corrected with respect to the true ground control points in order to produce complete SAR products. Speeded Up Robust Feature (SURF) technique is an efficient algorithm to extract ground control points from images but is considered to be inappropriate to apply to SAR images due to high speckle noises. In this paper, an attempt is made to apply SURF algorithm to SAR images for image registration and fusion. Matched points are extracted with respect to the varying parameters of Hessian and SURF matching thresholds, and the performance is analyzed by measuring the imaging matching accuracies. A number of performance measures concerning image registration are suggested to validate the use of SURF for spaceborne SAR images. Various simulations methodologies are suggested the validate the use of SURF for the geometric correction and image registrations and it is shown that a good choice of input parameters to the SURF algorithm should be made to apply for the spaceborne SAR images of moderate resolutions.

Object tracking in video sequences has broad applications in both military and civilian domains. However, as the length of input video sequence increases, a number of problems arise, such as severe object occlusion, object appearance variation, and object out-of-view (some portion or the entire object leaves the image space). To deal with these problems and identify the object being tracked from cluttered background, we present a robust appearance model using Speeded Up Robust Features (SURF) and advanced integrated features consisting of the Felzenszwalb's Histogram of Oriented Gradients (FHOG) and color attributes. Since re-detection is essential in long-term tracking, we develop an effective object re-detection strategy based on moving area detection. We employ the popular kernel correlation filters in our algorithm design, which facilitates high-speed object tracking. Our evaluation using the CVPR2013 Object Tracking Benchmark (OTB2013) dataset illustrates that the proposed algorithm outperforms reference state-of-the-art trackers in various challenging scenarios.

Tracking-by-detection methods have been widely studied and some promising results have been obtained. These methods use discriminative appearance models to train and update online classifiers. They also use a sliding window to detect samples which will then be classified. Then, the location of the sample with the maximum classifier response will be selected as the new location. Compressive tracking was recently proposed with an appearance model based on features extracted in the compressed domain. However, CT uses a fixed-size tracking box to detect samples, and this is unsuitable for practice applications. CT detects samples around the selected region of the previous frame within a fixed radius. Here, the classifier may become inaccurate if the selected region drifts. The fixed radius is also not suitable for tracking targets that experience abrupt acceleration changes. Furthermore, CT updates the classifier parameters with constant learning rate. If the target is fully occluded for an extended period, the classifier will instead learn the features of the cover object and the target will ultimately be lost. In this paper, we present a multi-scale compressive tracker. This tracker integrates an improved appearance model based on normalized rectangle features extracted in the adaptive compressive domain into the bootstrap filter. This type of feature extraction is efficient, and the computation complexity does not increase as the tracking regions become larger. The classifier response is utilized to generate particle importance weight and a re-sample procedure preserves samples according to weight. A 2-order transition model considers the target velocity to estimate the current position and scale status. In this way, the sampling is not limited to a fixed range. Here, feedback strategies are adopted to adjust learning rate for occlusion. Experimental results on various benchmark challenging sequences have demonstrated the superior performance of our tracker when compared with several state-of-the-art tracking algorithms.

How to construct compact and powerful feature descriptors is an important research subject in the fields of machine vision and pattern recognition. After the research on SURF, the issue that the Haar descriptor of Speeded Up Robust Features(SURF) algorithm can not make full use of the information around the neighborhood of the feature points is found. To resolve the issue, this paper proposes a improved SURF based on the original SURF algorithm. The algorithm makes full use of the information around the neighborhood of the sub-block by masking with 3*3 window templates and then constructs the descriptor which can obtain better discriminative power. In addition, aimed at the primary direction of the SURF depends too much on the local region, the paper improves the confirmation of the primary direction. At last, this paper combines the nearest distance and the ratio between the nearest distance and the second nearest distance to match the interest points. The experiment shows that the algorithm proposed in this paper has better discriminative power, and the face recognition rate is enhanced.

Enhancing discriminative appearance model for visual tracking

Copy-Move forgery is the most widely used way to tamper image for creating the forged images. The meaning of the photograph is changed by forging or concealing the images. Copy move is a very common technique for image forgery; tools used for retouching are applied along with it making naked eyes difficult to detect the copy move. Hence, the verification of authenticity and integrity is very essential of the image. The copy-move forgery detection can be divided into two heads a) Block based method b) Keypoint based. In the case of blocked based method, the method is exceptionally good in detecting the forged regions with higher accuracy but uses extremely high power of computation. Speed up Robust Feature (SURF) and Scale Invariant Feature Transform (SIFT) are invariant with respect to geometrical and illumination transform. In this paper we review the different keypoint approach which is a better approach or alternate to block-based approach due to invariancy property of SURF and SIFT. The keypoint based copy-move forgery detection schemes involve, identifying and relating the localized features of the images using the algorithms like SIFT and SURF.

We develop an algorithm for finding and kinematically tracking multiple people in long sequences. Our basic assumption is that people tend to take on certain canonical poses, even when performing unusual activities like throwing a baseball or figure skating. We build a person detector that quite accurately detects and localizes limbs of people in lateral walking poses. We use the estimated limbs from a detection to build a discriminative appearance model; we assume the features that discriminate a figure in one frame will discriminate the figure in other frames. We then use the models as limb detectors in a pictorial structure framework, detecting figures in unrestricted poses in both previous and successive frames. We have run our tracker on hundreds of thousands of frames, and present and apply a methodology for evaluating tracking on such a large scale. We test our tracker on real sequences including a feature-length film, an hour of footage from a public park, and various sports sequences. We find that we can quite accurately automatically find and track multiple people interacting with each other while performing fast and unusual motions.

Abnormal event detection and automated alarm are the important tasks in visual surveillance applications. In this paper, a novel automated alarm method based on intelligent visual analysis is proposed for alarm of abandoned objects and virtual cordon protection. Firstly the monitoring regions and cordons position are set artificially in the surveillance background scenes. The forground motion regions are segmented based on background subtraction model, and then are clustered by connected component analysis. After motion region segmentation and cluster, object tracking based on discriminative appearance model for monocular multi-target tracking is utilized. According to the motion segmentation and tracking results, alarm is triggered in comparison with the monitoring regions and cordons position. Experimental results show that the proposed automated alarm algorithms are sufficient to detect the abnormal events for alarm of abandoned objects and virtual cordon protection.

Video target tracking is a critical problem in the field of computer vision. Particle filters have been proven to be very useful in target tracking for nonlinear and non-Gaussian estimation problems. Although most existing algorithms are able to track targets well in controlled environments, it is often difficult to achieve automated and robust tracking of pedestrians in video sequences if there are various changes in target appearance or surrounding illumination. To surmount these difficulties, this paper presents multitarget tracking of pedestrians in video sequences based on particle filters. In order to improve the efficiency and accuracy of the detection, the algorithm firstly obtains target regions in training frames by combining the methods of background subtraction and Histogram of Oriented Gradient (HOG) and then establishes discriminative appearance model by generating patches and constructing codebooks using superpixel and Local Binary Pattern (LBP) features in those target regions. During the process of tracking, the algorithm uses the similarity between candidates and codebooks as observation likelihood function and processes severe occlusion condition to prevent drift and loss phenomenon caused by target occlusion. Experimental results demonstrate that our algorithm improves the tracking performance in complicated real scenarios.