Learning target-focusing convolutional regression model for visual object tracking

SiamDF: Tracking training data-free siamese tracker

Much progress has been made in siamese tracking, primarily benefiting from increasing huge training data. However, very little attention has been really paid to the role of huge training data in learning an effective siamese tracker. In this study, we undertake an in-depth analysis of this issue from a novel optimization perspective, and observe that training data is particularly adept at background suppression, thereby refining target representation. Inspired by this insight, we present a data-free siamese tracking algorithm named SiamDF, which requires only a pre-trained backbone and no further fine-tuning on additional training data. Particularly, to suppress background distractors, we separately improve two branches of siamese tracking by retaining the pure target region as target input with the removal of template background, and by exploring an efficient inverse transformation to maintain the constant aspect ratio of target state in search region. Besides, we further promote the center displacement prediction of the entire backbone by eliminating its spatial stride deviations caused by convolution-like quantification operations. Our experimental results on several popular benchmarks demonstrate that SiamDF, free from both offline fine-tuning and online update, achieves impressive performance compared to well-established unsupervised and supervised tracking methods.

Multiple object tracking with behavior detection in crowded scenes using deep learning

Multi-object tracking (MOT) is essential for solving the majority of computer vision issues related to crowd analytics. In an MOT system designing object detection and association are the two main steps. Every frame of the video stream is examined to find the desired objects in the first step. Their trajectories are determined in the second step by comparing the detected objects in the current frame to those in the previous frame. Less missing detections are made possible by an object detection system with high accuracy, which results in fewer segmented tracks. We propose a new deep learning-based model for improving the performance of object detection and object tracking in this research. First, object detection is performed by using the adaptive Mask-RCNN model. After that, the ResNet-50 model is used to extract more reliable and significant features of the objects. Then the effective adaptive feature channel selection method is employed for selecting feature channels to determine the final response map. Finally, an adaptive combination kernel correlation filter is used for multiple object tracking. Extensive experiments were conducted on large object tracking databases like MOT-20 and KITTI-MOTS. According to the experimental results, the proposed tracker performs better than other cutting-edge trackers when faced with various problems. The experimental simulation is carried out in python. The overall success rate and precision of the proposed algorithm are 95.36% and 93.27%.

Robust thermal infrared tracking via an adaptively multi-feature fusion model

When dealing with complex thermal infrared (TIR) tracking scenarios, the single category feature is not sufficient to portray the appearance of the target, which drastically affects the accuracy of the TIR target tracking method. In order to address these problems, we propose an adaptively multi-feature fusion model (AMFT) for the TIR tracking task. Specifically, our AMFT tracking method adaptively integrates hand-crafted features and deep convolutional neural network (CNN) features. In order to accurately locate the target position, it takes advantage of the complementarity between different features. Additionally, the model is updated using a simple but effective model update strategy to adapt to changes in the target during tracking. In addition, a simple but effective model update strategy is adopted to adapt the model to the changes of the target during the tracking process. We have shown through ablation studies that the adaptively multi-feature fusion model in our AMFT tracking method is very effective. Our AMFT tracker performs favorably on PTB-TIR and LSOTB-TIR benchmarks compared with state-of-the-art trackers.

Intelligent Trigonometric Particle Filter for visual tracking

Visual tracking is one of the pre-eminent tasks in several computer vision applications. Particle filter (PF) is extensively used in visual tracking for intelligent surveillance system applications, hugely significant. But the re-sampling procedure of PF will result in sample impoverishment, which will affect the precision of tracking simultaneously. In this paper, a new tracking technique, called Trigonometric Particle Filter (TPF), based on PF optimized by Sine Cosine Algorithm (SCA), which contains trigonometric sine and cosine functions, is proposed. An enhanced method for improving the number of target particles used in a Sine Cosine Algorithm for trigonometric particle filter includes SCA ahead of the re-sampling step. This step ensures a more extensive particle set Achievement of the proposed TPF tracker is inspected and assessed on Visual Tracker Benchmark (VOT) databases. The proposed TPF tracker is compared with evolutionary-based methods like the Spider monkey optimization assisted PF (SMO-PF), Firefly algorithm-based PF (FAPF) method, Particle swarm optimization-based PF (PSO-PF) and Particle filter, recent four correlation filter-based trackers, and also with other ten state-of-the-art tracking methods. We demonstrate that visual tracking using TPF delivers additional consistent and proficient tracking outcomes than compared trackers.

SiamATL: Online Update of Siamese Tracking Network via Attentional Transfer Learning

Visual object tracking with semantic deep features has recently attracted much attention in computer vision. Especially, Siamese trackers, which aim to learn a decision making-based similarity evaluation, are widely utilized in the tracking community. However, the online updating of the Siamese fashion is still a tricky issue due to the limitation, which is a tradeoff between model adaption and degradation. To address such an issue, in this article, we propose a novel attentional transfer learning-based Siamese network (SiamATL), which fully exploits the previous knowledge to inspire the current tracker learning in the decision-making module. First, we explicitly model the template and surroundings by using an attentional online update strategy to avoid template pollution. Then, we introduce an instance-transfer discriminative correlation filter (ITDCF) to enhance the distinguishing ability of the tracker. Finally, we suggest a mutual compensation mechanism that integrates cross-correlation matching and ITDCF detection into the decision-making subnetwork to achieve online tracking. Comprehensive experiments demonstrate that our approach outperforms state-of-the-art tracking algorithms on multiple large-scale tracking datasets.

Real-Time Ship Tracking under Challenges of Scale Variation and Different Visibility Weather Conditions

Visual ship tracking provides crucial kinematic traffic information to maritime traffic participants, which helps to accurately predict ship traveling behaviors in the near future. Traditional ship tracking models obtain a satisfactory performance by exploiting distinct features from maritime images, which may fail when the ship scale varies in image sequences. Moreover, previous frameworks have not paid much attention to weather condition interferences (e.g., visibility). To address this challenge, we propose a scale-adaptive ship tracking framework with the help of a kernelized correlation filter (KCF) and a log-polar transformation operation. First, the proposed ship tracker employs a conventional KCF model to obtain the raw ship position in the current maritime image. Second, both the previous step output and ship training sample are transformed into a log-polar coordinate system, which are further processed with the correlation filter to determine ship scale factor and to suppress the negative influence of the weather conditions. We verify the proposed ship tracker performance on three typical maritime scenarios under typical navigational weather conditions (i.e., sunny, fog). The findings of the study can help traffic participants efficiently obtain maritime situation awareness information from maritime videos, in real time, under different visibility weather conditions.

Deep Neural Network Regularization (DNNR) on Denoised Image

Image dehazing in supervised learning models suffers from overfitting and underfitting problems. To avoid overfitting, the authors use regularization techniques like dropout and L2 norm. Dropout helps in reducing overfitting and batch normalization reduces the training time. In this paper, they have conducted experiments to analyze combination of various hyperparameters to have better network performance using deep neural network (DNN) on cifar10 dataset. The qualitative and quantitative study is performed by estimating the accuracy of the model on training and test images using with and without batch normalization. The proposed model performs better and is more stable. The results shows that dropout regularization technique is better than L2 technique containing hidden layers with large neurons. The paper assesses performance of DNN for any denoised model with the techniques like batch normalization and dropout, feature map, and adding more layers to the network. The authors quantitatively identify the value model loss and accuracy with the absence and presence of these parameters.

Deep learning on image denoising: An overview

Deep learning techniques have received much attention in the area of image denoising. However, there are substantial differences in the various types of deep learning methods dealing with image denoising. Specifically, discriminative learning based on deep learning can ably address the issue of Gaussian noise. Optimization models based on deep learning are effective in estimating the real noise. However, there has thus far been little related research to summarize the different deep learning techniques for image denoising. In this paper, we offer a comparative study of deep techniques in image denoising. We first classify the deep convolutional neural networks (CNNs) for additive white noisy images; the deep CNNs for real noisy images; the deep CNNs for blind denoising and the deep CNNs for hybrid noisy images, which represents the combination of noisy, blurred and low-resolution images. Then, we analyze the motivations and principles of the different types of deep learning methods. Next, we compare the state-of-the-art methods on public denoising datasets in terms of quantitative and qualitative analyses. Finally, we point out some potential challenges and directions of future research.