Spatio-temporal Context Tracking Research Articles

Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking

Despite eminent progress in recent years, various challenges associated with object tracking algorithms such as scale variations, partial or full occlusions, background clutters, illumination variations are still required to be resolved with improved estimation for real-time applications. This paper proposes a robust and fast algorithm for object tracking based on spatio-temporal context (STC). A pyramid representation-based scale correlation filter is incorporated to overcome the STC’s inability on the rapid change of scale of target. It learns appearance induced by variations in the target scale sampled at a different set of scales. During occlusion, most correlation filter trackers start drifting due to the wrong update of samples. To prevent the target model from drift, an occlusion detection and handling mechanism are incorporated. Occlusion is detected from the peak correlation score of the response map. It continuously predicts target location during occlusion and passes it to the STC tracking model. After the successful detection of occlusion, an extended Kalman filter is used for occlusion handling. This decreases the chance of tracking failure as the Kalman filter continuously updates itself and the tracking model. Further improvement to the model is provided by fusion with average peak to correlation energy (APCE) criteria, which automatically update the target model to deal with environmental changes. Extensive calculations on the benchmark datasets indicate the efficacy of the proposed tracking method with state of the art in terms of performance analysis.

An improved spatio-temporal context tracking algorithm based on scale correlation filter

Spatio-temporal context algorithm is commonly used in geology and has been introduced into the field of target tracking in recent years. The algorithm improved the robustness of visual tracking through dense contextual information around the target and thus achieved great tracking results. However, updating errors may occur in spatio-temporal context algorithm when the target has rapid changes in scale and appearance, resulting in the algorithm cannot extract the target area accurately and completely. In order to overcome this problem, this article proposes an improved spatio-temporal context algorithm based on scale correlation filter. First of all, the algorithm extracts samples of different scales around the target after the target is settled by spatio-temporal context algorithm and then forms the pyramid of scale characteristics through samples extracted by histogram of oriented gradients operator. Second, the best scale parameter will be achieved by means of scale correlation filter to update the scale model. Finally, the results show that the algorithm has good tracking effect and robustness through two experiments which are contrast experiment before and after the improvement of algorithm and comparison experiment with other advanced algorithms.

基于嵌入式平台的低时间复杂度目标跟踪算法

Aimed at the application background of embedded platform which is often limited in computing power, this paper proposed a low time complexity target tracking algorithm, CTSTC algorithm, which was suitable for complex scenes. The algorithm consisted of two parts:the main part constituted by the spatio-temporal context target tracking based on adaptive update of model and the aided target location part constituted by compressive tracking based on adaptive update of model. When the results of spatio-temporal context tracking were unreliable, the aided location part was activated. If the results of aided location were reliable, the aided location result was used to correct the spatio-temporal context tracking part. The running speed of the algorithm was close to that of the spatio-temporal context learning algorithm (STC). The test on I5CPU can reach 1 577 frames per second, which was much faster than other commonly used algorithms. It was a very fast target tracking algorithm, but the robustness of the algorithm in complex environments was improved. Using OTB2013 data set to test, compared with STC algorithm, CTSTC accuracy increased by 12.8%, success rate increased by 27.5%. The algorithm is tested on a small target tracking system with DM6437 as the core, which can achieve real-time stable tracking.

Learning spatio-temporal context via hierarchical features for visual tracking

Spatio-temporal context (STC) based visual tracking algorithms have demonstrated remarkable tracking capabilities in recent years. In this paper, we propose an improved STC method, which seamlessly integrates capabilities of the powerful feature representations and mappings from the convolutional neural networks (CNNs) based on the theory of transfer learning. Firstly, the dynamic training confidence map, obtained from a mapping neural network using transferred CNN features, rather than the fixed training confidence map is utilized in our tracker to adapt the practical tracking scenes better. Secondly, we exploit hierarchical features from both the original image intensity and the transferred CNN features to construct context prior models. In order to enhance the accuracy and robustness of our tracker, we simultaneously transfer the fine-grained and semantic features from deep networks. Thirdly, we adopt the training confidence index (TCI), reflected from the dynamic training confidence map, to guide the updating process. It can determine whether back propagations should be conducted in the mapping neural network, and whether the STC model should be updated. The introduction of the dynamic training confidence map could effectively deal with the problem of location ambiguity further in our tracker. Overall, the comprehensive experimental results illustrate that the tracking capability of our tracker is competitive against several state-of-the-art trackers, especially the baseline STC tracker, on the existing OTB-2015 and UAV123 visual tracking benchmarks.

A Reliable and Real-Time Tracking Method with Color Distribution.

Occlusion is a challenging problem in visual tracking. Therefore, in recent years, many trackers have been explored to solve this problem, but most of them cannot track the target in real time because of the heavy computational cost. A spatio-temporal context (STC) tracker was proposed to accelerate the task by calculating context information in the Fourier domain, alleviating the performance in handling occlusion. In this paper, we take advantage of the high efficiency of the STC tracker and employ salient prior model information based on color distribution to improve the robustness. Furthermore, we exploit a scale pyramid for accurate scale estimation. In particular, a new high-confidence update strategy and a re-searching mechanism are used to avoid the model corruption and handle occlusion. Extensive experimental results demonstrate our algorithm outperforms several state-of-the-art algorithms on the OTB2015 dataset.

An Object Tracking Method Combined Spatio-temporal Context Learning with Color Features

The correlation filtering tracking approaches have attracted much attention because of its high-speed and robustness. For further improving the efficiency and robustness in tracking. We propose the usage of color features to construct the object appearance in the STC tracker. The combined feature of gray and color features can be computed efficiently and possess high discriminative power. Experiment results on two video sequences demonstrate that the spatio-temporal context tracker with color features can obtain superior performance in real-world tracking.

基于双目标模型的时空上下文跟踪算法

基于双目标模型的时空上下文跟踪算法

New Fast Fall Detection Method Based on Spatio-Temporal Context Tracking of Head by Using Depth Images

In order to deal with the problem of projection occurring in fall detection with two-dimensional (2D) grey or color images, this paper proposed a robust fall detection method based on spatio-temporal context tracking over three-dimensional (3D) depth images that are captured by the Kinect sensor. In the pre-processing procedure, the parameters of the Single-Gauss-Model (SGM) are estimated and the coefficients of the floor plane equation are extracted from the background images. Once human subject appears in the scene, the silhouette is extracted by SGM and the foreground coefficient of ellipses is used to determine the head position. The dense spatio-temporal context (STC) algorithm is then applied to track the head position and the distance from the head to floor plane is calculated in every following frame of the depth image. When the distance is lower than an adaptive threshold, the centroid height of the human will be used as the second judgment criteria to decide whether a fall incident happened. Lastly, four groups of experiments with different falling directions are performed. Experimental results show that the proposed method can detect fall incidents that occurred in different orientations, and they only need a low computation complexity.