Online Semantic Object Segmentation for Vision Robot Collected Video

Li, Kunqian; Tao, Wenbing; Liu, Liman

doi:10.1109/access.2019.2933479

Cited by 13 publications

(5 citation statements)

References 55 publications

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“…Data compression, image compression, audio/video compression, and telecommunications bandwidth compression are all examples of compression [15]. To take use of the inherent statistical redundancy in real-world photographs, various image compression methods have been developed [16]. Video compression is a procedure that modifies the frames components in order to reduce its overall size while maintaining its visual quality.…”

Section: Figure 1 Duplicate Video Frame Removal Processmentioning

confidence: 99%

Efficient Memory Handling Model with Consistent Video Frame Duplication Removal with Precise Compression

Sameerunnisa,

Jabez

2023

RIA

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Massive amounts of videos are being made and shared online as mobile devices and social networks gain popularity in recent years. The enormous expansion in the amount of video data created has made storing and quickly searching it all quite difficult. Because many movies are duplicates or near-duplicates in practice, recognizing these copies has become a critical strategy for decreasing the amount of storage with duplicate removal models. Video compression is an important part of Internet video delivery for efficient memory management. Deep learning's growth has sparked a revival in video compression, with many frameworks offering comparable or even higher performance than traditional video codecs presented in recent years. Despite the advancement in rate-distortion, these models are substantially slower and need more memory, limiting their practical application. The exponentially increasing volume of video data created has presented enormous problems to video deduplication technologies. People are interested in uploading and sharing information in photo and video formats in this digital era. This expansion has resulted in increased storage capacity, which contains a large amount of redundant multimedia material. Many deduplication algorithms are being rapidly developed nowadays, although they are often slow and have rather imprecise identification processes. Deduplication is one of the emerging ways for coping with redundant data stored in several locations. When more than a copy of the same data is detected, a single copy is preserved, and the other data is replaced by pointers pointing to the preserved copy and also duplicate frames will be removed by segmenting the video for memory efficiency. Storage can be utilised to effectively store a large amount of other data. While there are many other types of deduplication algorithms, picture and video deduplication strategies and implementations receive a lot of attention since they are difficult to implement. In this research a Consistent Video Frame Duplication Removal with Precise Compression (CVFDR-PC) model for efficient memory handling is proposed. This research provides a versatile and efficient video frame deduplication framework with compression model that effectively handles the memory. The proposed model when contrasted with the existing methods exhibit better performance levels.

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Section: Figure 1 Duplicate Video Frame Removal Processmentioning

confidence: 99%

Efficient Memory Handling Model with Consistent Video Frame Duplication Removal with Precise Compression

Sameerunnisa,

Jabez

2023

RIA

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“…Li, K et al [25] suggested a novel method known as the online video object segmentation approach, which conducts the segmentation by detection structure. This approach effectively segments the video frames with high accuracy; however, the computational time is high compared to other approaches.…”

Section: Related Workmentioning

confidence: 99%

Facial Image Segmentation by Integration of Level Set and Neural Network Optimization with Hybrid Filter Pre-processing Model

Rangayya¹,

Patil²,

Patil³

2021

Eng. Sci.

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Face segmentation is the process of segmenting the visible parts of the face excluding the neck, ears, hair, and beards. In this field, several methods have been developed, but none of them have been effective in providing optimal face segmentation. Hence, we proposed a novel face segmentation method known as level-set-based neural network (NN) algorithm. This method exploits a hybrid filter for the pre-processing of images, which eliminates the unwanted noises and blurring effect from the images. The hybrid filter is the combination of Median, Mean, and Gaussian filters and effectively removes the unwanted noises. Hence the images are segmented by utilizing level-set-based NN algorithm which is commonly based on the population set and effectively reduces the gap between the predicted and expected outcomes. The proposed method is compared with state-of-art methods such as Fully convolution network (FCN), Gabor filter(GF), multi-class semantic face segmentation(MSFS), and genetic algorithms (GA). From the experimental analysis, it is evident that the proposed work achieved better results comparing to other approaches.

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“…Object detection is the fundamental task in computer vision field, which is also closely related with many other vision tasks, such as object segmentation [25], [26], [45], [46], [50] and object tracking [4], [5], [13], [52]. For decades, object detection has receives tremendous development [60].…”

Section: A Object Detectionmentioning

confidence: 99%

Focus First: Coarse-to-Fine Traffic Sign Detection With Stepwise Learning

Liu

Wang

et al. 2020

IEEE Access

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Traffic sign detection is a very important part of intelligent assisted driving system. However, with the interference of various target sizes, geometric distortion, occlusion and motion blur, fast and accurate detection on large-size car camera image is extremely hard. To achieve both high efficient and accurate detection, we present a traffic sign detection method within a coarse-to-fine framework, which sequentially detects the targets in grid-level and image-level. We demonstrate that focusing first is a more effective detection strategy for small targets in wide detection space. We propose a target grid prediction network, which is a fully convolutional network for binary classification, to realize rapid coarse localization of the target and effectively guide the clipping and scaling of the target area. With the flexible potential target region extracting strategy, the detecting space can be significantly reduced. At the same time, the correctly extracted local areas for the targets can further facilitate the accurate detection of the subsequent traffic sign detector. In the experiments, our method achieves impressive performance in terms of both efficiency and accuracy. On the challenging Tsinghua-Tencent 100K (TT100K) dataset, our method achieves 20.9 FPS detection speed for 1600 × 1600 images and the F 1-score of the proposed method for all-scale targets is 91.55.

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Online Semantic Object Segmentation for Vision Robot Collected Video

Cited by 13 publications

References 55 publications

Efficient Memory Handling Model with Consistent Video Frame Duplication Removal with Precise Compression

Efficient Memory Handling Model with Consistent Video Frame Duplication Removal with Precise Compression

Facial Image Segmentation by Integration of Level Set and Neural Network Optimization with Hybrid Filter Pre-processing Model

Focus First: Coarse-to-Fine Traffic Sign Detection With Stepwise Learning

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