Object Tracking in Hyperspectral-Oriented Video with Fast Spatial-Spectral Features

Identifying and tracking objects in surveillance videos is an important means of mining information during surveillance. Currently, most object‐tracking methods rely only on image features, which cannot accurately express the motion of the tracked object in real geographical scenes and are easily influenced by occlusion and surrounding objects having similar features. However, tracked objects, such as pedestrians and vehicles, usually move in geographical space with fixed patterns of motion, and the motion in a short time is constrained by geographical space and time, the motion trajectory is predictable, and the range of motion is limited. Therefore, based on the SiamFC object tracking framework, this study introduces geographical spatiotemporal constraints into the tracking framework and proposes the GeoSiamFC method. The objective of this is to: (1) construct the mapping relationship between geographical space and image space to solve the problem that the pixel movement within the image after perspective imaging cannot accurately express the motion of the tracked object in a real geographical scene; (2) add candidate search areas according to the predicted trajectory location to correct the tracking errors caused by the occlusion of the object; and (3) focus on the search for the range of motion of the mapped pixel within the image space according to the limited geographical range of motion of the tracked objects in a certain time to reduce the interference of similar objects within the search area. In this study, separate experiments were conducted on a common test dataset using multiple methods to deal with challenges such as occlusion and illumination changes. In addition, a robust test dataset with noise addition and luminance adjustment based on the common test dataset was used. The results show that GeoSiamFC outperforms other object‐tracking methods in the common test dataset with a precision score of 0.995 and a success score of 0.756 compared with most other object‐tracking algorithms under the base condition of using only shallow networks. Moreover, GeoSiamFC maintained the highest precision score (0.970) and high success score (0.734) in the more challenging robust test dataset as well. The tracking speed of 59 frames per second far exceeds the real‐time requirement of 25 FPS. Geographical spatiotemporal constraints were considered to improve tracker performance while providing real‐time feedback on the motion trajectory of the target in geographical space. Thus, the proposed method is suitable for real‐time tracking of the motion trajectory of a target in real geographical scenes in various surveillance videos.

Video object segmentation and tracking are two essential building blocks of smart surveillance systems. However, there are several issues that need to be resolved. Threshold decision is a difficult problem for video object segmentation with a multi-background model. In addition, some conditions make robust video object tracking difficult. These conditions include nonrigid object motion, target appearance variations due to changes in illumination, and background clutter. In this paper, a video object segmentation and tracking framework is proposed for smart cameras in visual surveillance networks with two major contributions. First, we propose a robust threshold decision algorithm for video object segmentation with a multi-background model. Second, we propose a video object tracking framework based on a particle filter with the likelihood function composed of diffusion distance for measuring color histogram similarity and motion clue from video object segmentation. The proposed framework can track nonrigid moving objects under drastic changes in illumination and background clutter. Experimental results show that the presented algorithms perform well for several challenging sequences, and our proposed methods are effective for the aforementioned issues.

Air quality and reduction of emissions in the transport sector are determinant factors in achieving a sustainable global climate. The monitoring of emissions in traffic routes can help to improve route planning and to design strategies that may make the pollution levels to be reduced. In this work, a method which detects the pollution levels of transport vehicles from the images of IP cameras by means of computer vision techniques and neural networks is proposed. Specifically, for each sequence of images, a homography is calculated to correct the camera perspective and determine the real distance for each pixel. Subsequently, the trajectory of each vehicle is computed by applying convolutional neural networks for object detection and tracking algorithms. Finally, the speed in each frame and the pollution emitted by each vehicle are determined. Experimental results on several datasets available in the literature support the feasibility and scalability of the system as an emission control strategy.

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.

The thesis addresses the following challenging problems of detecting and tracking humans in the presence of occlusions in typical surveillance videos: (1) adaptation of semantic-part-based human detectors to new surveillance video sequence when trained detectors using other video data not performing well on the new video data; (2) tracking of humans with person identification minimizing identification errors over longer tracking periods; and (3) hierarchical spatial and temporal analysis for discriminative tracking of human targets. The thesis aims to improve the state-of-the-art performance in human detection and tracking by studying the human detectors, extended tracking of track segments (tracklets) generated from short term tracking of detection responses. For the adaptation of semantic-part-based human detectors to new surveillance video sequence, a uni ed deep CNN model for joint learning of features, semantic pedestrian part detectors and a transfer learning model is developed. The components within this deep CNN model interact with each other in the learning process, which facilitates the optimization of the learned components during the co-operative learning. In particular, an adaptation layer is proposed to embed the capability of knowledge transfer into the CNN model. As a result, the proposed transferred CNN (T-CNN) model is able to transfer the visual knowledge of the semantic pedestrian parts from the source data to target data. Extensive experimental evaluations show that the proposed method is better than other deep learning based methods in terms of detection performance. Moreover, the adaptive deep features can be complementary to the pre-defined features used by other state-of-the-art methods. For tracking of humans with person identification minimizing identification errors over longer tracking periods, a novel method, based on online target-specific metric learning and coherent dynamics estimation, for tracklet association by network flow optimization is developed. The proposed framework aims to exploit appearance and motion cues to prevent identity switches during tracking and also to recover missed detections. The target-specific metrics (appearance cue) and motion dynamics (motion cue) are proposed to be learned and estimated online, i.e. during the tracking process. Furthermore, a learning algorithm to learn the weights of motion and appearance tracking cues for tracklet affinity models is proposed to handle some difficult situations. Extensive evaluations following state-of-the-art practices have been conducted and the results from these evaluations show the improvements by the proposed method over some existing state-of-the-art methods. In hierarchical spatial and temporal analysis for discriminative tracking of human targets, inspired by recent advances in convolutional neural network (CNN) architectures, a novel uni ed deep model for tracklet association, which can jointly learn the CNNs and temporally constrained metrics, is developed. Furthermore, a novel loss function incorporating temporally constrained multi-task learning mechanism is developed to make the deep model more effective in solving the tracklet association problem. Extensive experimental results comparing with the state-of-the-art methods demonstrate the effectiveness and superiority of the proposed unified deep model.

Segmenting and tracking of objects in video is of great importance for video-based encoding, surveillance, and retrieval. However, the inherent difficulty of object segmentation and tracking is to distinguish changes in the displacement of objects from disturbing effects such as noise and illumination changes. Therefore, in this paper, we formulate a color-based deformable model which is robust against noisy data and changing illumination. Computational methods are presented to measure color constant gradients. Further, a model is given to estimate the amount of sensor noise through these color constant gradients. The obtained uncertainty is subsequently used as a weighting term in the deformation process. Experiments are conducted on image sequences recorded from three-dimensional scenes. From the experimental results, it is shown that the proposed color constant deformable method successfully finds object contours robust against illumination, and noisy, but homogeneous regions.

<p>The ultimate goal of this study is to afford enhanced video object detection and tracking by eliminating the limitations which are existing nowadays. Although high performance ratio for video object detection and tracking is achieved in the earlier work it takes more time for computation. Consequently we are in need to propose a novel video object detection and tracking technique so as to minimize the computational complexity. Our proposed technique covers five stages they are preprocessing, segmentation, feature extraction, background subtraction and hole filling. Originally the video clip in the database is split into frames. Then preprocessing is performed so as to get rid of noise, an adaptive median filter is used in this stage to eliminate the noise. The preprocessed image then undergoes segmentation by means of modified region growing algorithm. The segmented image is subjected to feature extraction phase so as to extract the multi features from the segmented image and the background image, the feature value thus obtained are compared so as to attain optimal value, consequently a foreground image is attained in this stage. The foreground image is then subjected to morphological operations of erosion and dilation so as to fill the holes and to get the object accurately as these foreground image contains holes and discontinuities. Thus the moving object is tracked in this stage. This method will be employed in MATLAB platform and the outcomes will be studied and compared with the existing techniques so as to reveal the performance of the novel video object detection and tracking technique. </p>

In our work describes a method for accurately tracking persons in indoor surveillance video stream obtained from a static camera with difficult scene properties including illumination changes and solves the major occlusion problem by using Kalman filtering. First, moving objects are precisely extracted by determining its motion, for further processing. The scene illumination changes are averaged to obtain the accurate moving object during background subtraction process. In case of objects occlusion, we use the color feature information to accurately distinguish between objects. The method is able to identify moving persons, track them and provide unique tag for the tracked persons. The effectiveness of the proposed method is demonstrated with experiments in an indoor environment.

This book chapter presents recently developed approaches for object tracking in video combining the structural similarity measure with particle filtering techniques.

Appearance change of moving targets is a challenging problem in visual tracking. In this paper, we present a novel visual object tracking algorithm based on the observation dependent hidden Markov model (OD-HMM) framework. The observation dependency is computed by structure complexity coefficients (SCC) which is defined to predict the target appearance change. Unlike conventional methods addressing the appearance change problem by investigating different online appearance models, we handle this problem by addressing the fundamental reason of motion -related appearance change during visual tracking. Based on the analysis of motion-related appearance change, we investigate the relationship between the structure of the object surface and the appearance stability. The appearance of complex structural regions is easier to change compared with that of smooth structural regions with object moving. Based on this, we define SCC to predict the appearance stability of moving objects. Different from the standard HMM-based tracking algorithms where observations between different frames are assumed to be independent, we consider the observation dependency between consecutive frames with the information provided by SCC. Moreover , we present a novel outlier removing method in appearance model updating which helps to avoid error accumulation. Experimental results on challenging video sequences demonstrate that the proposed visual tracking algorithm with OD-HMM and SCC achieves better performance than existing related tracking algorithms.

Despite remarkable progress, visual object tracking is still a challenging task as objects usually suffer from significant appearance changes, fast motion, and serious occlusion. In this paper, we propose an anti-occlusion correlation filter-based tracking method (AO-CF) for robust visual tracking. We first propose an occlusion criterion based on continuous response values. Based on the criterion, objects are divided into four categories to adaptively identify the occlusion of objects. Then we propose a new detection condition for detecting proposals. When the occlusion criterion is triggered, the re-detection mechanism is executed and the tracker is commanded to stop, and then the re-detector selects the most reliable proposal to reinitialize the tracker. Experimental results show that our method outperforms other state-of-the-art trackers in terms of both precision rate and success rate on the widely used object tracking benchmark dataset. In addition, AO-CF is able to achieve real-time tracking speed.

Significant progress is made in the field of object tracking recently. Especially, trackers based on deep learning and correlation filters both have achieved excellent performance. However, object tracking still faces some challenging problems such as deformation and illumination. In such kinds of situations, the accuracy and precision of tracking algorithms plunge as a result. It is imminent to find a solution to this situation. In this paper, we propose a tracking algorithm based on features extracted by residual network called Resnet features and cascaded correlation filters to improve precision and accuracy. Firstly, features extracted by a deep residual network trained on other image processing datasets, are robust enough and retain higher resolution, therefore, we exploit Resnet-101 pretrained offline to obtain features extracted by middle and high layers for target appearance model representation. Resnet-101 is deeper compared with other deep neural networks which means it contains more semantic information. Then, the method we propose to combine our correlation filters is superior. We propose cascaded correlation filters generated by handcraft, middle-level and high-level features from residual network to gain better competence. Handcraft features localize target precisely because they contain more spatial details while Resnet features are robust to the target appearance change because they retain more semantic information. Finally, we conduct extensive experiments on OTB2013 and OTB2015 benchmark. The experimental results show that our tracker achieves high performance under all kinds of challenges and performs favorably against other state-of-the-art trackers.

In order to achieve facial object detection and tracking in video, a method based on nonlinear sequence Monte Carlo filtering technology is proposed. The algorithm is simple, effective, and easy to operate, which can solve the problems of scale change and occlusion in the process of online learning tracking, so as to ensure the smooth implementation of learning effect evaluation. Experimental methods should be added to the article summary section. The results show that the algorithm in this study outperforms the basic KCF in terms of evaluation accuracy and success rate, as well as outperforms other tracker algorithms in benchmark, achieving scores of 0.837 and 0.705, respectively. In terms of overlapping accuracy, the reason why this study’s algorithm is higher than KCF is that this study determines the tracking status of the current target by calculating the primary side regulated (PSR) value when the target is obscured or lost, which does not make the tracking error to accumulate. The tracking algorithm in this study is not ranked first in the two attributes of motion blur and low resolution, but the rankings of all other nine attributes belong to the first. Compared with the KCF algorithm, the accuracy plots for the three attributes of scale change, occlusion, and leaving the field of view are improved by 10.26, 13.48, and 13.04%, respectively. Thus, it is proved that the method based on nonlinear sequence Monte Carlo filtering technology can achieve video facial object detection and tracking.

Soft Computing based object detection and tracking approaches: State-of-the-Art survey

Real time object tracking is an essential part of surveillance and robot applications. The performance of any object tracking system depends on its accuracy and its ability to deal with various sizes of objects, for better results the tracking should happen at a high speed. Object tracking can be defined as the process of segmenting the region of interest from the video sequence and keeping track of the motion in order to extract useful information. The basic fundamentals of video surveillance such as object detection, tracking and recognition using multiple cameras. Whenever an object enters a video frame, it is detected using background subtraction and the detected objects are tracked with a novel Bayesian Kalman filter with simplified Gaussian mixture (BKF-SGM). Object recognition algorithm is applied using Gait method which is employed to support multiple object tracking with its robust performance. Processing of image is done in MATLAB to get detection, tracking and recognized results.