Fast object detection based on selective visual attention

Guo, Minghui; Zhao, Yuzhou; Zhang, Chenbin; Chen, Zonghai

doi:10.1016/j.neucom.2014.04.054

Cited by 39 publications

(20 citation statements)

References 13 publications

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“…Saliency detection has been successfully applied to many computer vision problems such as object detection [16,22,17,49], image/video segmentation [18], image/video retrieval [58], video summarization [28] and action recognition [68].…”

Section: Introductionmentioning

confidence: 99%

Salient object detection in video using deep non-local neural networks

Shokri

Harati

Taba

2020

Journal of Visual Communication and Image Representation

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Detection of salient objects in image and video is of great importance in many computer vision applications. In spite of the fact that the state of the art in saliency detection for still images has been changed substantially over the last few years, there have been few improvements in video saliency detection. This paper investigates the use of recently introduced non-local neural networks in video salient object detection. Non-local neural networks are applied to capture global dependencies and hence determine the salient objects. The effect of non-local operations is studied separately on static and dynamic saliency detection in order to exploit both appearance and motion features. A novel deep non-local neural network architecture is introduced for video salient object detection and tested on two well-known datasets DAVIS and FBMS. The experimental results show that the proposed algorithm outperforms state-of-the-art video saliency detection methods.

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Section: Introductionmentioning

confidence: 99%

Salient object detection in video using deep non-local neural networks

Shokri

Harati

Taba

2020

Journal of Visual Communication and Image Representation

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“…Few changes have been made to the original framework except an extended branch called Selective Module for predicting saliency maps. Saliency is to filter the visual information and select interesting ones for further processing [10]. In our overall architecture, saliency maps are rapidly generated from the former shallow feature maps and guide the latter layers where to do calculation through masked-convolutions [16], where we extend saliency as a binary location-guided mask.…”

Section: Introductionmentioning

confidence: 99%

Selective Convolutional Network: An Efficient Object Detector with Ignoring Background

Ling

Qin

Zhang

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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It is well known that attention mechanisms can effectively improve the performance of many CNNs including object detectors. Instead of refining feature maps prevalently, we reduce the prohibitive computational complexity by a novel attempt at attention. Therefore, we introduce an efficient object detector called Selective Convolutional Network (SCN), which selectively calculates only on the locations that contain meaningful and conducive information. The basic idea is to exclude the insignificant background areas, which effectively reduces the computational cost especially during the feature extraction. To solve it, we design an elaborate structure with negligible overheads to guide the network where to look next. It's end-to-end trainable and easy-embedding. Without additional segmentation datasets, we explores two different train strategies including direct supervision and indirect supervision. Extensive experiments assess the performance on PASCAL VOC2007 and MS COCO detection datasets. Results show that SSD and Pelee integrated with our method averagely reduce the calculations in a range of 1/5 and 1/3 with slight loss of accuracy, demonstrating the feasibility of SCN.

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“…Salient object detection from videos plays an important role as a pre-processing step in many computer vision applications such as video re-targeting [1], object detection [2], person reidentication [3], and visual tracking [4]. Conventional methods for salient object detection often segment each frame into regions and artificially combine low-level (bottom-up) features (e.g., intensity [5], color [5], edge orientation [6]) with heuristic (top-down) priors (e.g., center prior [7], boundary prior [5], objectness [6]) detected from the regions.…”

Section: Introductionmentioning

confidence: 99%

Video Salient Object Detection Using Spatiotemporal Deep Features

Sugimoto

2018

IEEE Trans. on Image Process.

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This paper presents a method for detecting salient objects in videos, where temporal information in addition to spatial information is fully taken into account. Following recent reports on the advantage of deep features over conventional handcrafted features, we propose a new set of spatiotemporal deep (STD) features that utilize local and global contexts over frames. We also propose new spatiotemporal conditional random field (STCRF) to compute saliency from STD features. STCRF is our extension of CRF to the temporal domain and describes the relationships among neighboring regions both in a frame and over frames. STCRF leads to temporally consistent saliency maps over frames, contributing to accurate detection of salient objects' boundaries and noise reduction during detection. Our proposed method first segments an input video into multiple scales and then computes a saliency map at each scale level using STD features with STCRF. The final saliency map is computed by fusing saliency maps at different scale levels. Our experiments, using publicly available benchmark datasets, confirm that the proposed method significantly outperforms the state-of-the-art methods. We also applied our saliency computation to the video object segmentation task, showing that our method outperforms existing video object segmentation methods.

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Fast object detection based on selective visual attention

Cited by 39 publications

References 13 publications

Salient object detection in video using deep non-local neural networks

Salient object detection in video using deep non-local neural networks

Selective Convolutional Network: An Efficient Object Detector with Ignoring Background

Video Salient Object Detection Using Spatiotemporal Deep Features

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