Interpolation-Based Object Detection Using Motion Vectors for Embedded Real-time Tracking Systems

Ujiie, Takayuki; Hiromoto, Masayuki; Satō, Takashi

doi:10.1109/cvprw.2018.00104

Cited by 20 publications

(40 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: B Work In the Compressed Domainmentioning

confidence: 99%

“…In order to use the motion information provided in the compressed domain freely, a few works [8], [17]- [19] have been done in the compressed domain. Ujiie et al [8] interpolated the bounding-boxes of objects in the bounding-box level for some frames to avoid detection and data association. Alvar et al [17] constructed approximate bounding-box of the target object in the pixel level based on the bounding-box in previous frame for single object tracking, but each frame needs to be restored into RGB image for detection.…”

Section: B Work In the Compressed Domainmentioning

confidence: 99%

“…Secondly, the motion information is readily provided in the compressed domain which is helpful for video tasks. A few works, such as tracking [8], [17], video object detection [18] and action recognition [19], have been done in the compressed domain. Generally, there are two goals of the implementations in the compressed domain: (1) Feature propagation [18].…”

Section: Introductionmentioning

confidence: 99%

“…Computational cost can be saved since the frequency of feature extraction is reduced and the feature propagation is much more efficient than feature extraction. (2) Motion cues extraction [8], [17], [19]. The motion cues of objects are directly extracted from the motion information (motion vectors and residuals, more specifically) without access to the RGB images, which are further used to handle the task.…”

Section: Introductionmentioning

confidence: 99%

“…Existing compressed domain based tracking algorithms focus on the motion cues extraction from the pixel level [17] or the bounding-box level [8]. For the pixel level, each pixel that locates in the bounding-box is shifted separately based on the motion vectors (MVs), then the smallest axis-aligned rectangle that includes all the shifted pixels is selected as the new bounding-box.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Real-Time Online Multi-Object Tracking in Compressed Domain

Liu

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Recent online multi-object tracking (MOT) methods have achieved desirable tracking performance. However, the tracking speed of most existing methods is rather slow. Inspired from the fact that the adjacent frames are highly relevant and redundant, we divide the frames into key and non-key frames and track objects in the compressed domain. For the key frames, the RGB images are restored for detection and data association. To make data association more reliable, an appearance convolutional neural network (CNN) which can be jointly trained with the detector is proposed. For the non-key frames, the objects are directly propagated by a tracking CNN based on the motion information provided in the compressed domain. Compared with the state-of-the-art online MOT methods, our tracker is about 6× faster while maintaining a comparable tracking performance.INDEX TERMS Compressed domain, multi-object tracking, online, real-time.

show abstract

Section: B Work In the Compressed Domainmentioning

confidence: 99%

Section: B Work In the Compressed Domainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Real-Time Online Multi-Object Tracking in Compressed Domain

Liu

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

IoT based monitoring of air quality and traffic using regression analysis

Martín-Baos

Rodríguez-Benítez

García-Ródenas

et al. 2022

Applied Soft Computing

View full text Add to dashboard Cite

Deep learning in video multi-object tracking: A survey

et al. 2020

View full text Add to dashboard Cite

The problem of Multiple Object Tracking (MOT) consists in following the trajectory of different objects in a sequence, usually a video. In recent years, with the rise of Deep Learning, the algorithms that provide a solution to this problem have benefited from the representational power of deep models. This paper provides a comprehensive survey on works that employ Deep Learning models to solve the task of MOT on single-camera videos. Four main steps in MOT algorithms are identified, and an in-depth review of how Deep Learning was employed in each one of these stages is presented. A complete experimental comparison of the presented works on the three MOTChallenge datasets is also provided, identifying a number of similarities among the top-performing methods and presenting some possible future research directions.In this section, a general description about the problem of MOT is provided. The main characteristics and common steps of MOT algorithms are identified and described in section 2.1. The metrics that are usually employed to evaluate the performance of the models are discussed in section 2.2, while the most important benchmark datasets are presented in section 2.3. 7 The website says the detections were obtained using a model based on a latent SVM, or L-SVM. That model is now known as Deformable Parts Model (DPM). 8

show abstract

Interpolation-Based Object Detection Using Motion Vectors for Embedded Real-time Tracking Systems

Cited by 20 publications

References 20 publications

Real-Time Online Multi-Object Tracking in Compressed Domain

Real-Time Online Multi-Object Tracking in Compressed Domain

IoT based monitoring of air quality and traffic using regression analysis

Deep learning in video multi-object tracking: A survey

Contact Info

Product

Resources

About