Automatic target detection and tracking in forward-looking infrared image sequences using morphological connected operators

Braga-Neto, Ulisses; Choudhary, Manish; Goutsias, John

doi:10.1117/1.1789982

Cited by 85 publications

(6 citation statements)

References 11 publications

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“…In the work presented by Choudhary [ 48 ], an automated process for the identification and tracking of targets within sequences of infrared images was achieved through the application of morphological connected operators. It involved two stages: intraframe and interframe.…”

Section: Taxonomy and Review The Small Object Detection And Tracking ...mentioning

confidence: 99%

Small Object Detection and Tracking: A Comprehensive Review

Mirzaei

Nezamabadi‐pour

Raoof

et al. 2023

Sensors

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Object detection and tracking are vital in computer vision and visual surveillance, allowing for the detection, recognition, and subsequent tracking of objects within images or video sequences. These tasks underpin surveillance systems, facilitating automatic video annotation, identification of significant events, and detection of abnormal activities. However, detecting and tracking small objects introduce significant challenges within computer vision due to their subtle appearance and limited distinguishing features, which results in a scarcity of crucial information. This deficit complicates the tracking process, often leading to diminished efficiency and accuracy. To shed light on the intricacies of small object detection and tracking, we undertook a comprehensive review of the existing methods in this area, categorizing them from various perspectives. We also presented an overview of available datasets specifically curated for small object detection and tracking, aiming to inform and benefit future research in this domain. We further delineated the most widely used evaluation metrics for assessing the performance of small object detection and tracking techniques. Finally, we examined the present challenges within this field and discussed prospective future trends. By tackling these issues and leveraging upcoming trends, we aim to push forward the boundaries in small object detection and tracking, thereby augmenting the functionality of surveillance systems and broadening their real-world applicability.

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Section: Taxonomy and Review The Small Object Detection And Tracking ...mentioning

confidence: 99%

Small Object Detection and Tracking: A Comprehensive Review

Mirzaei

Nezamabadi‐pour

Raoof

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…A receiver operating characteristic (ROC) curve is plotted using these two metrics. In this work, the proposed method is compared with two different algorithms, namely Zhang's method [32] and Braga-Neto's method [33].…”

Section: Related Workmentioning

confidence: 99%

“…No comparison was described [31] Collected by the authors Correct detection rate and false alarm rate Compared with [32,33]. [34] No benchmark Euclidean and city block distance, false-negative rate, and true-positive rate Compared with KLT [35] [36]…”

Section: Workmentioning

confidence: 99%

Beyond Land: A Review of Benchmarking Datasets, Algorithms, and Metrics for Visual-Based Ship Tracking

Rocha

Figueiredo

2023

Electronics

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Object tracking has gained much interest in the last few years, especially in the context of multiple object tracking. Many datasets used for tracking provide video sequences of people and objects in very different contexts. Although it has been attracting much attention, no dataset or tracking algorithm has been applied to coastal surveillance and ship tracking. Besides image/video-based tracking technologies, other technologies, such as radar and automatic identification systems (AISs), are also used for this task, especially in maritime applications. In the AIS case, commonly known issues, such as information omission, remain to be dealt with. As for radars, the most important issue is the impossibility of identifying the ship type/class and correlating it with AIS information. However, image/video-based solutions can be combined with these technologies to mitigate or even solve these issues. This work aims to review the most recent datasets and state-of-the-art tracking algorithms (also known as trackers) for single or multiple objects tracking for objects in general and its possibilities for maritime scenarios. The goal is to gain insights for developing novel datasets; benchmarking metrics; and mainly, novel ship tracking algorithms.

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“…Target detection aims to detect the object of interest in the image and determine its category and location. It has been widely used in X-ray image detection [1], traffic sign recognition [2], intelligent recognition monitoring [3], industrial detection [4] and other fields. With the breakthrough of deep learning algorithm in image classification [5][6], target detection algorithm has developed from the traditional algorithm based on manual feature extraction [7][8][9] to the algorithm based on deep learning [10][11][12].The target detection algorithm based on deep learning overcomes the problems of relying on artificial experience and poor robustness in traditional methods, and its detection accuracy has been significantly improved.…”

Section: Introductionmentioning

confidence: 99%

Research on Object Detection Method Based on FF-YOLO for Complex Scenes

2021

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YOLO v3 has poor accuracy in target location recognition, and the detection effect needs to be improved in complex scenes with dense target distribution and large size differences. To solve this problem, an improved multi-scale target detection algorithm based on feature fusion (FF-YOLO) is proposed in this paper. Firstly, the residual structure in Darknet53 backbone of YOLO v3 is replaced by the optimized dense connection network FCN-DenseNet, and features are extracted effectively through feature reuse, and the problem of vanishing gradient is further alleviated. Secondly, based on the three-scale prediction mechanism of YOLO v3, we added a fourth detection scale to make the network learn more shallower location information. Finally, Spatial Pyramid Pooling (SPP) module is added before each detection layer to make the local feature information deeply fused.It increases the receptive field of the backbone network and significantly isolates the most important contextual feature. Experiments show that FF-YOLO can effectively improve the detection accuracy of multi-scale targets in complex scenes. On Pascal VOC2007 data set, the mAP of FF-YOLO is 5.8% higher than that of YOLO v3. At the same time, the mAP of FF-YOLO for medium and small targets are 1.5% and 2.2% higher than that of YOLO v3 on MS COCO data set.

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Automatic target detection and tracking in forward-looking infrared image sequences using morphological connected operators

Cited by 85 publications

References 11 publications

Small Object Detection and Tracking: A Comprehensive Review

Small Object Detection and Tracking: A Comprehensive Review

Beyond Land: A Review of Benchmarking Datasets, Algorithms, and Metrics for Visual-Based Ship Tracking

Research on Object Detection Method Based on FF-YOLO for Complex Scenes

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