Small Target Tracking Research Articles

YOLO-PBESW: A Lightweight Deep Learning Model for the Efficient Identification of Indomethacin Crystal Morphologies in Microfluidic Droplets.

Crystallization is important to the pharmaceutical, the chemical, and the materials fields, where the morphology of crystals is one of the key factors affecting the quality of crystallization. High-throughput screening based on microfluidic droplets is a potent technique to accelerate the discovery and development of new crystal morphologies with active pharmaceutical ingredients. However, massive crystal morphologies' datum needs to be identified completely and accurately, which is time-consuming and labor-intensive. Therefore, effective morphologies' detection and small-target tracking are essential for high-efficiency experiments. In this paper, a new improved algorithm YOLOv8 (YOLO-PBESW) for detecting indomethacin crystals with different morphologies is proposed. We enhanced its capability in detecting small targets through the integration of a high-resolution feature layer P2, and the adoption of a BiFPN structure. Additionally, in this paper, adding the EMA mechanism before the P2 detection head was implemented to improve network attention towards global features. Furthermore, we utilized SimSPPF to replace SPPF to mitigate computational costs and reduce inference time. Lastly, the CIoU loss function was substituted with WIoUv3 to improve detection performance. The experimental findings indicate that the enhanced YOLOv8 model attained advancements, achieving AP metrics of 93.3%, 77.6%, 80.2%, and 99.5% for crystal wire, crystal rod, crystal sheet, and jelly-like phases, respectively. The model also achieved a precision of 85.2%, a recall of 83.8%, and an F1 score of 84.5%, with a mAP of 87.6%. In terms of computational efficiency, the model's dimensions and operational efficiency are reported as 5.46 MB, and it took 12.89 ms to process each image with a speed of 77.52 FPS. Compared with state-of-the-art lightweight small object detection models such as the FFCA-YOLO series, our proposed YOLO-PBESW model achieved improvements in detecting indomethacin crystal morphologies, particularly for crystal sheets and crystal rods. The model demonstrated AP values that exceeded L-FFCA-YOLO by 7.4% for crystal sheets and 3.9% for crystal rods, while also delivering a superior F1-score. Furthermore, YOLO-PBESW maintained a lower computational complexity, with parameters of only 11.8 GFLOPs and 2.65 M, and achieved a higher FPS. These outcomes collectively demonstrate that our method achieved a balance between precision and computational speed.

DCANet: Dense Convolutional Attention Network for infrared small target detection

Infrared small target detection and tracking technology plays a essential role in systems like infrared warning, guidance, and missile defense. Efficiently and reliably detecting fast-moving, small targets is a key technological challenge. This study introduces a dense convolutional attention network as a potential solution to address the identified challenge. This network incorporates a dense convolutional interaction module that effectively minimizes information loss across different levels. Additionally, a simple, parameter-free attention module is introduced to adaptively extract features and enhance the emphasis on important information. Furthermore, a dense pixel contrastive learning module is introduced to capture more features of targets with similar sizes by constructing positive and negative sample pairs and designing a pixel contrastive loss function. Experimental validation was conducted on four representative datasets, demonstrating the success of our approach in terms of performance superiority over other methods.

A cross-fusion of non-local attention network for infrared small target tracking

Infrared small target tracking plays important roles in many applications, such as infrared surveillance and reconnaissance. However, achieving promising performance with existing trackers is difficult due to the following challenges: (1) Infrared small targets lack color and texture information, making it difficult for the network to capture effective features; (2) A slight centroid-shift of the target can lead to tracking failure. To overcome these challenges, this paper proposes the CFNLA network, which consists of two parts: (1) The cross-fusion module is designed to capture the relationship between the target and its surroundings, including a non-local block to enhance the feature information of the target at the current scale and reduce interference such as background; (2) The confidence loss function is proposed to improve tracking stability by forcing the network to concentrate on centroid shift. Furthermore, a self-designed infrared small target tracking dataset, IRSTT, is constructed to evaluate the proposed algorithm. Finally, the experimental results on various open-source datasets and the IRSTT dataset verify the superior performance of the CFNLA network.

A Method for Top View Pedestrian Flow Detection Based on Small Target Tracking

A Method for Top View Pedestrian Flow Detection Based on Small Target Tracking

Detection and Tracking of Small Targets in Remote Sensing Images

In the context of remote sensing imagery, the accurate detection and tracking of small targets pose significant challenges. This study delves into the intricate task of identifying and monitoring these minute targets within such images. To address this, we embark on a comprehensive investigation of existing methodologies, with the intention to not only understand their intricacies but also to replicate their results. In addition, our project takes a step further by introducing a visually intuitive interface. This interface serves as a valuable tool, enhancing the usability of our work by providing a platform for efficient small target detection and tracking. Through this combined effort of research, replication, and innovation, our study aims to contribute to the advancement of target detection and tracking in remote sensing images.

Dim Small Target Detection and Tracking: A Novel Method Based on Temporal Energy Selective Scaling and Trajectory Association

Dim Small Target Detection and Tracking: A Novel Method Based on Temporal Energy Selective Scaling and Trajectory Association

Probability Hypothesis Density Correlation Filter for Infrared Small Target Tracking

Probability Hypothesis Density Correlation Filter for Infrared Small Target Tracking

A Driver Fatigue Detection Algorithm Based on Dynamic Tracking of Small Facial Targets Using YOLOv7

A Driver Fatigue Detection Algorithm Based on Dynamic Tracking of Small Facial Targets Using YOLOv7

SiamIST: Infrared small target tracking based on an improved SiamRPN

The continuous tracking of infrared dim-small targets is significant, due to limited spatial resolution and low thermal features. Tracking algorithms based correlation filter may perform not well referring to infrared information. Therefore, a Deep Learning (DL) model is proposed for the tracking task with public data sets of small targets. To be specific, the Siamese Region Proposal Network (SiamRPN) is improved by the style recalibration module, which can obtain the perception of image styles. Furthermore, the proposed algorithm takes advantage of transfer learning technology referring to labeled target images, obtaining good features. To distinguish the small target from the background edges, the side window filtering is combined with the improved SiamRPN model. The experimental results show the good performance of the proposed small target tracking, namely SiamIST, in public near-infrared videos, compared to several related algorithms. Importantly, the designed algorithm uses the DL model to track small infrared targets for the first time, achieving an overall precision of 78.8%.

An improved dynamic programming tracking-before-detection algorithm based on LSTM network value function

The detection and tracking of small and weak maneuvering radar targets in complex electromagnetic environments is still a difficult problem to effectively solve. To address this problem, this paper proposes a dynamic programming tracking-before-detection method based on long short-term memory (LSTM) network value function(VL-DP-TBD). With the help of the estimated posterior probability provided by the designed LSTM network, the calculation of the posterior value function of the traditional DP-TBD algorithm can be more accurate, and the detection and tracking effect achieved for maneuvering small and weak targets is improved. Utilizing the LSTM network to model the posterior probability estimation of the target motion state, the posterior probability moving features of the maneuvering target can be learned from the noisy input data. By incorporating these posterior probability estimation values into the traditional DP-TBD algorithm, the accuracy and robustness of the calculation of the posterior value function can be enhanced, so that the improved architecture is capable of effectively recursively accumulating the movement trend of the target. Simulation results show that the improved architecture is able to effectively reduce the aggregation effect of a posterior value function and improve the detection and tracking ability for non-cooperative nonlinear maneuvering dim small target. Abbreviations LSTM: Long short-term memory; DP-TBD: Dynamic programming-based tracking before detection; DBT: Detection before tracking; TBD: Tracking before detection; HT-TBD: Tracking-before-detection algorithm based on the Hough transform; PF-TBD: Tracking-before-detection algorithm based on particle filtering; RFS-TBD: Tracking-before-detection algorithm based on random finite sets; SNR: Signal-to-noise ratio; DP: Dynamic programming; EVT: Extreme value theory; EVT: Generalized extreme value theory; GLRT: Generalized likelihood ratio detection; KT: Keystone transformation; PGA: Phase gradient autofocusing; CFAR: Constant false-alarm rate; J-CA-CFAR: Joint intensity-spatial CFAR; MF: Merit function; CP-DP-TBD: Candidate plot-based DP-TBD; CIT: Coherent integration time; RNN: Recurrent neural network; CS: Current statistical; Pd: Detection probability; Pt: Tracking probability.

An improved dynamic programming tracking-before-detection algorithm based on LSTM network

The detection and tracking of small and weak maneuvering radar targets in complex electromagnetic environments is still a difficult problem to effectively solve. To address this problem, this paper proposes a dynamic programming tracking-before-detection method based on a long short-term memory (LSTM) network (LSTM-DP-TBD). With the predicted target motion state provided by the LSTM network, the state transition range of the traditional DP-TBD algorithm can be updated in real time, and the detection and tracking effect achieved for maneuvering small and weak targets is also improved. Utilizing the LSTM network to model the moving state of the target, the moving features of the maneuvering target can be learned from the noisy input data. By incorporating these features into the traditional DP-TBD algorithm, the state transition set can be adjusted in time with the changes in the moving state of the target so that the new algorithm is capable of effectively recursively accumulating the movement trend of the maneuvering small and weak target. Simulation results show that the new algorithm is able to effectively accomplish the task of detecting and tracking maneuvering small and weak targets, and it achieves improved detection and tracking probabilities.

IRSDT: A Framework for Infrared Small Target Tracking with Enhanced Detection.

Currently, infrared small target detection and tracking under complex backgrounds remains challenging because of the low resolution of infrared images and the lack of shape and texture features in these small targets. This study proposes a framework for infrared vehicle small target detection and tracking, comprising three components: full-image object detection, cropped-image object detection and tracking, and object trajectory prediction. We designed a CNN-based real-time detection model with a high recall rate for the first component to detect potential object regions in the entire image. The KCF algorithm and the designed lightweight CNN-based target detection model, which parallelly lock on the target more precisely in the target potential area, were used in the second component. In the final component, we designed an optimized Kalman filter to estimate the target's trajectory. We validated our method on a public dataset. The results show that the proposed real-time detection and tracking framework for infrared vehicle small targets could steadily track vehicle targets and adapt well in situations such as the temporary disappearance of targets and interference from other vehicles.

Correlation Filtering Algorithm of Infrared Spectral Data for Dim Target Tracking

The correlation filtering algorithm of infrared spectral data for dim and small target tracking is studied to improve the tracking accuracy of small and weak targets and to track small and weak targets in real time. After the image noise reduction processing by the mean shift filtering algorithm, the infrared small and weak target image data model is constructed by using the denoised infrared small and weak target image. And the brightness value and position of unknown small and weak targets are obtained. The tracking and measurement model of small and weak targets is built. The joint probabilistic data association algorithm is used to calculate the probability that each measurement is associated with its possible source targets, and the particle filter is used to update the tracking status of small and weak targets to achieve real-time tracking of small and weak targets. The experimental results show that the algorithm can enhance the edge contour information of small and weak images, so as to accurately track small and weak targets moving in any track, and has good real-time tracking and accuracy. There is a small deviation between the tracking track of weak and small targets tracked by the algorithm and the actual track, and the root mean square difference of tracking weak and small targets is within 2 pixels. In addition, the detection probability of detecting weak and small targets is less affected by SNR environmental factors.

High-order time lacunarity feature-aided multiple hypotheses tracking for underwater active small targets in high-clutter harbor environment

Active tracking of underwater small targets is a great challenge with kinematic information alone. This is because the active sonar often encounters multipath propagation and the induced clutter can even mask target echoes. Recently, high-order time lacunarity (HOT-Lac) has shown its ability in effectively highlighting "blob" targets from high clutter harbor environments. Hence, this paper proposes a HOT-Lac aided track scoring mechanism to solve the ambiguity of data association within the framework of Multiple Hypotheses Tracking (MHT). Specifically, the trajectory consistency of potential targets is captured by a momentum accumulation of the HOT Lac feature, which can inherit the historical information for the whole track. Meanwhile, due to the separability of the distribution of target and clutter in the HOT-Lac feature space, the probabilities of the target hypothesis and null hypothesis are modeled by the online computation of the HOT-Lac feature. Finally, the cumulative likelihood ratio based on HOT-Lac is integrated into MHT to score the potential tracks. Experiments in several real-world harbor scenarios demonstrate that the proposed HOT-Lac feature-aided tracker can suppress false tracks accurately and quickly.

A Video Target Tracking and Correction Model with Blockchain and Robust Feature Location

In this paper, a cutting-edge video target tracking system is proposed, combining feature location and blockchain technology. The location method makes full use of feature registration and received trajectory correction signals to achieve high accuracy in tracking targets. The system leverages the power of blockchain technology to address the challenge of insufficient accuracy in tracking occluded targets, by organizing the video target tracking tasks in a secure and decentralized manner. To further enhance the accuracy of small target tracking, the system uses adaptive clustering to guide the target location process across different nodes. In addition, the paper also presents an unmentioned trajectory optimization post-processing approach, which is based on result stabilization, effectively reducing inter-frame jitter. This post-processing step plays a crucial role in maintaining a smooth and stable track of the target, even in challenging scenarios such as fast movements or significant occlusions. Experimental results on CarChase2 (TLP) and basketball stand advertisements (BSA) datasets show that the proposed feature location method is better than the existing methods, achieving a recall of 51% (27.96+) and a precision of 66.5% (40.04+) in the CarChase2 dataset and recall of 85.52 (11.75+)% and precision of 47.48 (39.2+)% in the BSA dataset. Moreover, the proposed video target tracking and correction model performs better than the existing tracking model, showing a recall of 97.1% and a precision of 92.6% in the CarChase2 dataset and an average recall of 75.9% and mAP of 82.87% in the BSA dataset, respectively. The proposed system presents a comprehensive solution for video target tracking, offering high accuracy, robustness, and stability. The combination of robust feature location, blockchain technology, and trajectory optimization post-processing makes it a promising approach for a wide range of video analytics applications, such as surveillance, autonomous driving, and sports analysis.

Infrared Small Target Tracking Algorithm via Segmentation Network and Multistrategy Fusion

To solve the problem of infrared (IR) small target tracking loss or error caused by factors such as scale changes, motion blur, occlusion, etc., this paper proposes a multi-strategy fusion tracking algorithm using an IR small target segmentation network as the detection head, which mainly includes six strategies: target pixel clustering, target feature threshold adjustment, large area search, small area tracking, gate tracking, and coordinate solution. First, candidate targets are obtained through the IR small target segmentation network and pixel clustering strategy. Second, the range of candidate targets is further reduced through threshold adjustment strategies. Then, real-time tracking of IR small targets is achieved through large area search, small area tracking, and wave gate tracking strategies. Finally, the longitude, latitude, and altitude of the tracked target are obtained through coordinate calculation strategies. Both qualitative and quantitative experiments based on real IR small target sequences verify that our algorithm can achieve more satisfactory performances in terms of success rate, precision, and robustness compared with other typical visual trackers. In addition, we have deployed tracking algorithms on the Orange Pi 5 embedded platform, and the tracking speed meets the real-time requirements.

Infrared Small Target Tracking Based on OSTrack Model

Infrared Small Target Tracking Based on OSTrack Model

An Intelligent Particle Filter for Infrared Dim Small Target Detection and Tracking

With consideration of low tracking accuracy and even losing target when the track-before-detect method based on particle filter (PF-TBD) tracks infrared dim small target in the complex background. In this article, a track-before-detect method based on the spring model firefly algorithm optimization particle filter (SFA-PF-TBD) was proposed to address this problem. First, the attractiveness and movement behavior of fireflies were introduced into the particle filter for optimizing the particles, and the optimization strength was controlled by evaluating the real-time distribution of particles. After optimization, detecting the density of the particles around the optimal particle, the elastic mechanism of spring was used to control the density of particles around the optimal particle when the particles gathered excessively, which made the distribution of particles more reasonable. Then, the TBD method was realized by the improved particle filter for tracking dim small target under the low signal-noise ratio conditions. Finally, we compared the SFA-PF-TBD algorithm with other algorithms by tracking experiments in simulation scenes and actual scenes. The results showed that the SFA-PF-TBD algorithm has more advantages than the PF-TBD algorithm.

Siamese hierarchical feature fusion transformer for efficient tracking.

Object tracking is a fundamental task in computer vision. Recent years, most of the tracking algorithms are based on deep networks. Trackers with deeper backbones are computationally expensive and can hardly meet the real-time requirements on edge platforms. Lightweight networks are widely used to tackle this issue, but the features extracted by a lightweight backbone are inadequate for discriminating the object from the background in complex scenarios, especially for small objects tracking task. In this paper, we adopted a lightweight backbone and extracted features from multiple levels. A hierarchical feature fusion transformer (HFFT) was designed to mine the interdependencies of multi-level features in a novel model-SiamHFFT. Therefore, our tracker can exploit comprehensive feature representations in an end-to-end manner, and the proposed model is capable of handling small target tracking in complex scenarios on a CPU at a rate of 29 FPS. Comprehensive experimental results on UAV123, UAV123@10fps, LaSOT, VOT2020, and GOT-10k benchmarks with multiple trackers demonstrate the effectiveness and efficiency of SiamHFFT. In particular, our SiamHFFT achieves good performance both in accuracy and speed, which has practical implications in terms of improving small object tracking performance in the real world.

DP–MHT–TBD: A Dynamic Programming and Multiple Hypothesis Testing-Based Infrared Dim Point Target Detection Algorithm

The detection and tracking of small targets under low signal-to-clutter ratio (SCR) has been a challenging task for infrared search and track (IRST) systems. Track-before-detect (TBD) is a widely-known algorithm which can solve this problem. However, huge computation costs and storage requirements limit its application. To address these issues, a dynamic programming (DP) and multiple hypothesis testing (MHT)-based infrared dim point target detection algorithm (DP–MHT–TBD) is proposed. It consists of three parts. (1) For each pixel in current frame, the second power optimal merit function-based DP is designed and performed in eight search areas to find the target search area that contains the real target trajectory. (2) In the target search area, the parallel MHT model is designed to save the tree-structured trajectory space, and a two-stage strategy is designed to mitigate the contradiction between the redundant trajectories and the requirements of more trajectories under low SCR. After constant false alarm segmentation of the energy accumulation map, the preliminary candidate points can be obtained. (3) The target tracking method is designed to eliminate false alarms. In this work, an efficient second power optimal merit function-based DP is designed to find the target search area for each pixel, which greatly reduces the trajectory search space. A two-stage MHT model, in which pruning for the tree-structured trajectory space is avoided and all trajectories can be processed in parallel, is designed to further reduce the hypothesis space exponentially. This model greatly reduces computational complexity and saves storage space, improving the engineering application of the TBD method. The DP–MHT–TBD not only takes advantage of the small computation amount of DP and high accuracy of an exhaustive search but also utilizes a novel structure. It can detect a single infrared point target when the SCR is 1.5 with detection probability above 90% and a false alarm rate below 0.01%.