Target Tracking Using Kalman Filter Based Algorithms

Kalman filter and its families have played an important role in information gathering, such as target tracking. Data association techniques have also been developed to allow the Kalman filter to track multiple targets simultaneously. This paper revisits the principle and applications of the Kalman filter for single target tracking and multiple hypothesis tracking (MHT) for multiple target tracking. We present the brief review of the Bayes filter family and introduce a brief derivation of the Kalman filter and MHT. We show examples for both single and multiple targets tracking in simulation to illustrate the efficacy of Kalman filter-based algorithms in target tracking scenarios.

Visual tracking using multi-layer appearance approach

This paper investigated single target tracking of arbitrary objects. Tracking is a difficult problem due to a variety of challenges such as scale variation, motion, background clutter, illumination etc. To achieve better tracking performance under these severe conditions, this paper proposed covariance descriptor based on multi-layer instance search region. Our results show that the proposed approach significantly improves the performance in term of centre location error (in pixels) compared to covariance descriptor with using a fixed bounding box. From this work, it is believed that we have constructed a great solution in choosing best layer for this descriptor. This will be addressed in the next future work such as consider target motion during tracking.

MultiRPN-DIDNet: Multiple RPNs and Distance-IoU Discriminative Network for Real-Time UAV Target Tracking

Target tracking in low-altitude Unmanned Aerial Vehicle (UAV) videos faces many technical challenges due to the relatively small sizes, various orientation changes of the objects and diverse scenes. As a result, the tracking performance is still not satisfactory. In this paper, we propose a real-time single-target tracking method with multiple Region Proposal Networks (RPNs) and Distance-Intersection-over-Union (Distance-IoU) Discriminative Network (DIDNet), namely MultiRPN-DIDNet, in which ResNet50 is used as the backbone network for feature extraction. Firstly, an instance-based RPN suitable for the target tracking task is constructed under the framework of Simases Neural Network. RPN is to perform bounding box regression and classification, in which channel attention mechanism is integrated to improve the representative capability of the deep features. The RPNs built on the Block 2, Block 3 and Block 4 of ResNet50 output their own Regression (Reg) coefficients and Classification scores (Cls) respectively, which are weighted and then fused to determine the high-quality region proposals. Secondly, a DIDNet is designed to correct the candidate target’s bounding box finely through the fusion of multi-layer features, which is trained with the Distance-IoU loss. Experimental results on the public datasets of UAV20L and DTB70 show that, compared with the state-of-the-art UAV trackers, the proposed MultiRPN-DIDNet can obtain better tracking performance with fewer region proposals and correction iterations. As a result, the tracking speed has reached 33.9 frames per second (FPS), which can meet the requirements of real-time tracking tasks.

Multitarget Tracking Using Siamese Neural Networks

In this article, we detect and track visual objects by using Siamese network or twin neural network. The Siamese network is constructed to classify moving objects based on the associations of object detection network and object tracking network, which are thought of as the two branches of the twin neural network. The proposed tracking method was designed for single-target tracking, which implements multitarget tracking by using deep neural networks and object detection. The contributions of this article are stated as follows. First, we implement the proposed method for visual object tracking based on multiclass classification using deep neural networks. Then, we attain multitarget tracking by combining the object detection network and the single-target tracking network. Next, we uplift the tracking performance by fusing the outcomes of the object detection network and object tracking network. Finally, we speculate on the object occlusion problem based on IoU and similarity score, which effectively diminish the influence of this issue in multitarget tracking.

A Method of Real-temporal Object Tracking Combined the Temporal Information and Spatial Information

The purpose of single target tracking is to accurately and continuously locate a specific object when it is moving. However, when the objects encounter with fast movement, severe occlusion, too small size, and the same local features, the tracking algorithm which based on correlation filter or convolutional neural network will appear the positioning error phenomenon. Aiming at the above problems, this paper designs a single target tracking algorithm: relative temporal spatial network (RTSnet). RTSnet is a multi-thread network that composed of Relative temporal Information Network (RTInet) and Relative Spatial Information Network (RSInet). RTInet is designed on the basis of LSTM, and it has the predictable characteristics of temporal. It mainly obtains the relative temporal information between the frames before and after the target. RSInet, an improved twin network based on the Triplet Network, has the effect of similarity determination which can to obtain the spatial information between the frames before and after the target. In the experiments, the RTSnet is trained by using LASOT data set and verified by using the LASOT test set and The OTB100 data set. In the test set of LASOT, the accuracy of RTSnet reaches 85.5%, StruckSiam reaches 50% and STRCF reaches 54%. Meanwhile, its tracking speed reaches 120fps due to the RTSnet adopts dual-thread operation. On the OTB100 data-set, the accuracy of RTSnet is 81.1%.

Distributed Kalman Filtering Based on the Non-Repeated Diffusion Strategy

Estimation accuracy is the core performance index of sensor networks. In this study, a kind of distributed Kalman filter based on the non-repeated diffusion strategy is proposed in order to improve the estimation accuracy of sensor networks. The algorithm is applied to the state estimation of distributed sensor networks. In this sensor network, each node only exchanges information with adjacent nodes. Compared with existing diffusion-based distributed Kalman filters, the algorithm in this study improves the estimation accuracy of the networks. Meanwhile, a single-target tracking simulation is performed to analyze and verify the performance of the algorithm. Finally, by discussion, it is proved that the algorithm exhibits good all-round performance, not only regarding estimation accuracy.