Deep Learning-Based Target Tracking and Classification for Low Quality Videos Using Coded Aperture Cameras

Kwan, Chiman; Chou, Bryan; Yang, Jonathan T.; Rangamani, Akshay; Tran, Trac D.; Zhang, Jack; Etienne‐Cummings, Ralph

doi:10.3390/s19173702

Cited by 29 publications

(22 citation statements)

References 21 publications

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“…Gan et al [41] developed an immature green citrus detection approach based on faster RCNN using color and thermal cameras. Kwan et al [42] [43] [44] firstly applied the YOLO algorithm on coded exposure cameras and achieved good detection accuracy with low power consumption and small bandwidth. Object detection is out of focus for our own application but focusing on low power consumption is also important even in agricultural application.…”

Section: Related Workmentioning

confidence: 99%

Optical Filter Net: A Spectral-Aware RGB Camera Framework for Effective Green Pepper Segmentation

Kurihara

Shu

2021

IEEE Access

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Detecting and identifying objects of similar color is a challenging task in computer vision. Green peppers in a natural environment can be found using the abundant information provided by a hyperspectral camera in the spectral domain, but the hyperspectral camera is an expensive device. Therefore, we propose a novel framework called Optical Filter Net, which enables the design of an optical filter that improves the performance of green pepper segmentation by a specific red-green-blue (RGB) camera system. When installed with the optical filter, the system can efficiently utilize the spectral information in the visible wavelength to distinguish green pepper and foliage without requiring an expensive hyperspectral camera. A main finding is the similarity between the transmission curve of the optical filter and the depth-wise convolution kernel without bias. Accordingly, we can treat the transmission curve of the optical filter as one layer of a deep neural network. The whole structure can be trained in an end-to-end manner. To comply with the physical requirement of the optical filter, we further constrain the training process to achieve a non-negative and smooth transmission curve. In an experimental evaluation on our dataset, our proposed spectral-aware RGB camera system outperformed the RGB camera system without an optical filter.INDEX TERMS Optical filter, transmission curve, green pepper segmentation, deep neural network

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Section: Related Workmentioning

confidence: 99%

Optical Filter Net: A Spectral-Aware RGB Camera Framework for Effective Green Pepper Segmentation

Kurihara

Shu

2021

IEEE Access

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“…One is called subsampling and the other is called coded aperture. The coded aperture case has been summarized and reported in our recent papers [37][38].…”

Section: Compressive Sensingmentioning

confidence: 99%

Deep Learning Based Target Tracking and Classification Directly in Compressive Measurement for Low Quality Videos

Kwan¹,

Chou²,

Yang³

et al. 2019

SIPIJ

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Past research has found that compressive measurements save data storage and bandwidth usage. However, it is also observed that compressive measurements are difficult to be used directly for target tracking and classification without pixel reconstruction. This is because the Gaussian random matrix destroys the target location information in the original video frames. This paper summarizes our research effort on target tracking and classification directly in the compressive measurement domain. We focus on one type of compressive measurement using pixel subsampling. That is, the compressive measurements are obtained by randomly subsample the original pixels in video frames. Even in such special setting, conventional trackers still do not work well. We propose a deep learning approach that integrates YOLO (You Only Look Once) and ResNet (residual network) for target tracking and classification in low quality videos. YOLO is for multiple target detection and ResNet is for target classification. Extensive experiments using optical and mid-wave infrared (MWIR) videos in the SENSIAC database demonstrated the efficacy of the proposed approach.

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“…For instance, there are some conventional target tracking methods [1,2]. In addition, some target detection and classification schemes using deep learning algorithms such as You Only Look Once (YOLO) for larger objects in short-range optical and infrared videos have been proposed in the literature [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. There are also some recent papers on moving target detection in thermal imagers [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…The use of YOLO is not very effective for long-range videos in which the targets are too small to have any discernible textures. Some recent algorithms [3][4][5][6][7][8][9][10][11][12][13] incorporated compressive measurements directly for detection and classification. Real-time issues have been discussed in [21].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Small Moving Target Detection Performance in Low-Quality and Long-Range Infrared Videos Using Optical Flow Techniques

Kwan

Budavari

2020

Remote Sensing

Self Cite

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The detection of small moving objects in long-range infrared videos is challenging due to background clutter, air turbulence, and small target size. In this paper, we summarize the investigation of efficient ways to enhance the performance of small target detection in long-range and low-quality infrared videos containing moving objects. In particular, we focus on unsupervised, modular, flexible, and efficient methods for target detection performance enhancement using motion information extracted from optical flow methods. Three well-known optical flow methods were studied. It was found that optical flow methods need to be combined with contrast enhancement, connected component analysis, and target association in order to be effective for target detection. Extensive experiments using long-range mid-wave infrared (MWIR) videos from the Defense Systems Information Analysis Center (DSIAC) dataset clearly demonstrated the efficacy of our proposed approach.

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Deep Learning-Based Target Tracking and Classification for Low Quality Videos Using Coded Aperture Cameras

Cited by 29 publications

References 21 publications

Optical Filter Net: A Spectral-Aware RGB Camera Framework for Effective Green Pepper Segmentation

Optical Filter Net: A Spectral-Aware RGB Camera Framework for Effective Green Pepper Segmentation

Deep Learning Based Target Tracking and Classification Directly in Compressive Measurement for Low Quality Videos

Enhancing Small Moving Target Detection Performance in Low-Quality and Long-Range Infrared Videos Using Optical Flow Techniques

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