Co-difference based object tracking algorithm for infrared videos

Demir, H. Seçkin; Çetin, A. Enis

doi:10.1109/icip.2016.7532394

Cited by 18 publications

(7 citation statements)

References 19 publications

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“…In Danelljan et al (2015), authors propose a discriminative correlation filter based approach (SRDCF) where they use a spatial regularization function that penalizes filter coefficients residing outside the target region. In Demir and Cetin (2016), authors propose a "co-difference" feature-based tracking algorithm (CODIFF) to efficiently represent and match image parts. This idea is further extended in Demir and Adil (2018) by including a part based approach (P-CODIFF) to achieve robustness against rotations and shape deformations.…”

Section: Related Workmentioning

confidence: 99%

Revealing Sea Turtle Behavior in Relation to Fishing Gear Using Color-Coded Spatiotemporal Motion Patterns With Deep Neural Networks

et al. 2021

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Incidental capture, or bycatch, of marine species is a global conservation concern. Interactions with fishing gear can cause mortality in air-breathing marine megafauna, including sea turtles. Despite this, interactions between sea turtles and fishing gear—from a behavior standpoint—are not sufficiently documented or described in the literature. Understanding sea turtle behavior in relation to fishing gear is key to discovering how they become entangled or entrapped in gear. This information can also be used to reduce fisheries interactions. However, recording and analyzing these behaviors is difficult and time intensive. In this study, we present a machine learning-based sea turtle behavior recognition scheme. The proposed method utilizes visual object tracking and orientation estimation tasks to extract important features that are used for recognizing behaviors of interest with green turtles (Chelonia mydas) as the study subject. Then, these features are combined in a color-coded feature image that represents the turtle behaviors occurring in a limited time frame. These spatiotemporal feature images are used along a deep convolutional neural network model to recognize the desired behaviors, specifically evasive behaviors which we have labeled “reversal” and “U-turn.” Experimental results show that the proposed method achieves an average F1 score of 85% in recognizing the target behavior patterns. This method is intended to be a tool for discovering why sea turtles become entangled in gillnet fishing gear.

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Section: Related Workmentioning

confidence: 99%

Revealing Sea Turtle Behavior in Relation to Fishing Gear Using Color-Coded Spatiotemporal Motion Patterns With Deep Neural Networks

et al. 2021

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“…One group contains conventional algorithms that utilize supervised machine-learning algorithms. For instance, there are some conventional target tracking methods [1,2]. The second group of target detection and classification schemes uses deep-learning algorithms such as You Only Look Once (YOLO) for larger objects in short-range optical and infrared videos [3][4][5][6][7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Converting Optical Videos to Infrared Videos Using Attention GAN and Its Impact on Target Detection and Classification Performance

et al. 2021

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To apply powerful deep-learning-based algorithms for object detection and classification in infrared videos, it is necessary to have more training data in order to build high-performance models. However, in many surveillance applications, one can have a lot more optical videos than infrared videos. This lack of IR video datasets can be mitigated if optical-to-infrared video conversion is possible. In this paper, we present a new approach for converting optical videos to infrared videos using deep learning. The basic idea is to focus on target areas using attention generative adversarial network (attention GAN), which will preserve the fidelity of target areas. The approach does not require paired images. The performance of the proposed attention GAN has been demonstrated using objective and subjective evaluations. Most importantly, the impact of attention GAN has been demonstrated in improved target detection and classification performance using real-infrared videos.

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“…It should be noted that the aforementioned papers detect targets frame by frame. Parallel to the above small target detection activities, there are some conventional target tracking methods [7,8] for videos. In general, target detection performance in videos can yield better results because target motion can be exploited.…”

Section: Introductionmentioning

confidence: 99%

Detection of Small Moving Objects in Long Range Infrared Videos from a Change Detection Perspective

Kwan

Larkin

2021

Photonics

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Detection of small moving objects in long range infrared (IR) videos is challenging due to background clutter, air turbulence, and small target size. In this paper, we present two unsupervised, modular, and flexible frameworks to detect small moving targets. The key idea was inspired by change detection (CD) algorithms where frame differences can help detect motions. Our frameworks consist of change detection, small target detection, and some post-processing algorithms such as image denoising and dilation. Extensive experiments using actual long range mid-wave infrared (MWIR) videos with target distances beyond 3500 m from the camera demonstrated that one approach, using Local Intensity Gradient (LIG) only once in the workflow, performed better than the other, which used LIG in two places, in a 3500 m video, but slightly worse in 4000 m and 5000 m videos. Moreover, we also investigated the use of synthetic bands for target detection and observed promising results for 4000 m and 5000 m videos. Finally, a comparative study with two conventional methods demonstrated that our proposed scheme has comparable performance.

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Co-difference based object tracking algorithm for infrared videos

Cited by 18 publications

References 19 publications

Revealing Sea Turtle Behavior in Relation to Fishing Gear Using Color-Coded Spatiotemporal Motion Patterns With Deep Neural Networks

Revealing Sea Turtle Behavior in Relation to Fishing Gear Using Color-Coded Spatiotemporal Motion Patterns With Deep Neural Networks

Converting Optical Videos to Infrared Videos Using Attention GAN and Its Impact on Target Detection and Classification Performance

Detection of Small Moving Objects in Long Range Infrared Videos from a Change Detection Perspective

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