Single Object Tracking Applied to an Aircraft

Ellouze, Ameni; Ksantini, Mohamed; Delmotte, François; Karray, Mohamed

doi:10.1109/ssd.2018.8570663

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Extracting features related to black smoke emissions from vehicles and combining them with classifiers can enable automatic detection of black smoke. Among the various methods, deep neural networks have been used to build object detection models that are categorized into two-stage and single-stage models [1][2][3][4]. Cao et al [5] utilized the Incep-tionv3 convolutional neural network to capture spatial information in surveillance videos with suspected black smoke frames, while a long short-term memory network learned the temporal dependencies between frames.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Model for Real-Time Detection of Vehicle Black Smoke

Chen,

Wang,

Zhai

2023

Sensors

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This paper discusses the application of deep learning technology in recognizing vehicle black smoke in road traffic monitoring videos. The use of massive surveillance video data imposes higher demands on the real-time performance of vehicle black smoke detection models. The YOLOv5s model, known for its excellent single-stage object detection performance, has a complex network structure. Therefore, this study proposes a lightweight real-time detection model for vehicle black smoke, named MGSNet, based on the YOLOv5s framework. The research involved collecting road traffic monitoring video data and creating a custom dataset for vehicle black smoke detection by applying data augmentation techniques such as changing image brightness and contrast. The experiment explored three different lightweight networks, namely ShuffleNetv2, MobileNetv3 and GhostNetv1, to reconstruct the CSPDarknet53 backbone feature extraction network of YOLOv5s. Comparative experimental results indicate that reconstructing the backbone network with MobileNetv3 achieved a better balance between detection accuracy and speed. The introduction of the squeeze excitation attention mechanism and inverted residual structure from MobileNetv3 effectively reduced the complexity of black smoke feature fusion. Simultaneously, a novel convolution module, GSConv, was introduced to enhance the expression capability of black smoke features in the neck network. The combination of depthwise separable convolution and standard convolution in the module further reduced the model’s parameter count. After the improvement, the parameter count of the model is compressed to 1/6 of the YOLOv5s model. The lightweight vehicle black smoke real-time detection network, MGSNet, achieved a detection speed of 44.6 frames per second on the test set, an increase of 18.9 frames per second compared with the YOLOv5s model. The mAP@0.5 still exceeded 95%, meeting the application requirements for real-time and accurate detection of vehicle black smoke.

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Section: Introductionmentioning

confidence: 99%

A Lightweight Model for Real-Time Detection of Vehicle Black Smoke

Chen,

Wang,

Zhai

2023

Sensors

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“…Novelty detection tackles an important unsupervised learning problem where novelty samples are not known a priori and the majority of the training dataset consists of "normal" data [1]. This problem has been widely applied in many areas, including abnormality detection [2,3], intruder detection [4], biomedical data processing [5], imbalance learning [6], vehicle tracking [7], and specific sign detection [8]. Different from other machine learning tasks, methods for one-class novelty detection are trained on only one class (i.e., the normal class) and aim to determine whether the given sample is a novelty during the inference stage.…”

Section: Introductionmentioning

confidence: 99%

Complex Background Reconstruction for Novelty Detection

Zhao,

Su,

et al. 2023

Applied Sciences

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Novelty detection aims to detect samples from classes different from the training samples (i.e., the normal class). Existing approaches predominantly make the target reconstruction better and choose the appropriate reconstruction error measurement method but ignore the influence of background information on this process. This paper proposes a novel reconstruction network and mutual information Siamese network. The reconstructed network aims to make the distribution of reconstructed samples consistent with that of original samples, intending to reduce background interference in the reconstruction process. After this, we measure the distance between the original and generated images based on a mutual information Siamese network, which extracts more discriminative features to calculate the similarity between the original images and their reconstructed ones. This part of the network uses global context information to improve the detection accuracy. We conduct extreme experiments to evaluate the proposed solution on two challenging public datasets. The experimental results show that the proposed method significantly outperforms the state-of-the-art methods.

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“…Another method is suggested based on an online fusion of trackers [8]. Some of the approaches are developed to deal with special problems in a tracking scenario such as occlusions [9], [10] while others extend the tracking task to a specific application [11], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Single Object Tracking through a Fast and Effective Single-Multiple Model Convolutional Neural Network

Lotfi,

Taghirad

2021

Preprint

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Object tracking becomes critical especially when similar objects are present in the same area. Recent state-ofthe-art (SOTA) approaches are proposed based on taking a matching network with a heavy structure to distinguish the target from other objects in the area which indeed drastically downgrades the performance of the tracker in terms of speed. Besides, several candidates are considered and processed to localize the intended object in a region of interest for each frame which is time-consuming. In this article, a special architecture is proposed based on which in contrast to the previous approaches, it is possible to identify the object location in a single shot while taking its template into account to distinguish it from the similar objects in the same area. In brief, first of all, a window containing the object with twice the target size is considered. This window is then fed into a fully convolutional neural network (CNN) to extract a region of interest (RoI) in a form of a matrix for each of the frames. In the beginning, a template of the target is also taken as the input to the CNN. Considering this RoI matrix, the next movement of the tracker is determined based on a simple and fast method. Moreover, this matrix helps to estimate the object size which is crucial when it changes over time. Despite the absence of a matching network, the presented tracker performs comparatively with the SOTA in challenging situations while having a super speed compared to them (up to 120F P S on 1080ti). To investigate this claim, a comparison study is carried out on the GOT-10k dataset. Results reveal the outstanding performance of the proposed method in fulfilling the task.

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Single Object Tracking Applied to an Aircraft

Cited by 5 publications

References 7 publications

A Lightweight Model for Real-Time Detection of Vehicle Black Smoke

A Lightweight Model for Real-Time Detection of Vehicle Black Smoke

Complex Background Reconstruction for Novelty Detection

Single Object Tracking through a Fast and Effective Single-Multiple Model Convolutional Neural Network

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