Compressively Sensed Image Recognition

Degerli, Aysen; Aslan, Sinem; Yamaç, Mehmet; Sankur, B.; Gabbouj, Moncef

doi:10.1109/euvip.2018.8611657

Cited by 21 publications

(28 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…] presents an online reconstruction free approach to object classification using compressed measurements. Similar to [29]- [31] and [35], the approach assumes the object is already at the center of the image. The methods in [29]- [31], [35] and [36] also did not address Instead of using Gaussian random measurements to obtain the compressive measurements, we emphasize that we have proposed two alternative compressive measurements.…”

Section: Compressive Sensingmentioning

confidence: 99%

See 1 more Smart Citation

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

Kwan¹,

Chou²,

Yang³

et al. 2019

SIPIJ

View full text Add to dashboard Cite

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.

show abstract

Section: Compressive Sensingmentioning

confidence: 99%

“…In [35], the authors present an approach to extracting features out of the compressed measurements and then uses the features to create a proxy image. This approach may not be considered as a reconstruction free approach.…”

Section: Introductionmentioning

confidence: 99%

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

Kwan¹,

Chou²,

Yang³

et al. 2019

SIPIJ

View full text Add to dashboard Cite

show abstract

“…In [25], the authors present an approach to extracting features out of the compressed measurements and then uses the features to create a proxy image, which is then used for action recognition. If our interpretation is correct, this approach may not be considered as a reconstruction free approach because there is a construction of a proxy image.…”

Section: Introductionmentioning

confidence: 99%

“…Paper [26] presents an online reconstruction free approach to object classification using compressed measurements. Similar to [19,20,21,25], the approach assumes the object is already at the center of the image. For an image frame where the target location is unknown, then it is not clear on how this approach can be applied to handle the above situation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Kwan

Chou

Yang

et al. 2019

Sensors

View full text Add to dashboard Cite

Compressive sensing has seen many applications in recent years. One type of compressive sensing device is the Pixel-wise Code Exposure (PCE) camera, which has low power consumption and individual control of pixel exposure time. In order to use PCE cameras for practical applications, a time consuming and lossy process is needed to reconstruct the original frames. In this paper, we present a deep learning approach that directly performs target tracking and classification in the compressive measurement domain without any frame reconstruction. In particular, we propose to apply You Only Look Once (YOLO) to detect and track targets in the frames and we propose to apply Residual Network (ResNet) for classification. Extensive simulations using low quality optical and mid-wave infrared (MWIR) videos in the SENSIAC database demonstrated the efficacy of our proposed approach.

show abstract

A high-performance approach to detecting small targets in long-range low-quality infrared videos

Kwan

Budavari

2021

SIViP

View full text Add to dashboard Cite

Since targets are small in long range infrared (IR) videos, it is challenging to accurately detect targets in those videos. In this paper, we propose a high performance approach to detecting small targets in long range and low quality infrared videos. Our approach consists of a video resolution enhancement module, a proven small target detector based on local intensity and gradient (LIG), a connected component (CC) analysis module, and a track association module to connect detections from multiple frames. Extensive experiments using actual mid-wave infrared (MWIR) videos in ranges between 3500 m and 5000 m from a benchmark dataset clearly demonstrated the efficacy of the proposed approach.

show abstract

Compressively Sensed Image Recognition

Cited by 21 publications

References 12 publications

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

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

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

A high-performance approach to detecting small targets in long-range low-quality infrared videos

Contact Info

Product

Resources

About