SGE net: Video object detection with squeezed GRU and information entropy map
Recently, deep learning based video object detection has attracted more and more attention. Compared with object detection of static images, video object detection is more challenging due to the motion of objects, while providing rich temporal information. The RNN-based algorithm is an effective way to enhance detection performance in videos with temporal information. However, most studies in this area only focus on accuracy while ignoring the calculation cost and the number of parameters. In this paper, we propose an efficient method that combines channel-reduced convolutional GRU (Squeezed GRU), and Information Entropy map for video object detection (SGE-Net). The experimental results validate the accuracy improvement, computational savings of the Squeezed GRU, and superiority of the information entropy attention mechanism on the classification performance. The mAP has increased by 3.7 contrasted with the baseline, and the number of parameters has decreased from 6.33 million to 0.67 million compared with the standard GRU.
Code (0)
등록된 구현이 없습니다.
Tasks
Objectobject-detectionObject DetectionVideo Object DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Squeezed Edge YOLO: Onboard Object Detection on Edge Devices
Demand for efficient onboard object detection is increasing due to its key role in autonomous navigation. However, deploying object detection models such as YOLO on resource constrained edge devices is challenging due to…
Autonomous NavigationObjectobject-detectionObject DetectionA Framework of Transfer Learning in Object Detection for Embedded Systems
Transfer learning is one of the subjects undergoing intense study in the area of machine learning. In object recognition and object detection there are known experiments for the transferability of parameters, but not for…
Hyperparameter OptimizationObjectobject-detectionObject Detection+2Learning spatio-temporal representations with temporal squeeze pooling
In this paper, we propose a new video representation learning method, named Temporal Squeeze (TS) pooling, which can extract the essential movement information from a long sequence of video frames and map it into a set o…
Action RecognitionClassificationGeneral ClassificationRepresentation Learning+1SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
Object detection is a crucial task for autonomous driving. In addition to requiring high accuracy to ensure safety, object detection for autonomous driving also requires real-time inference speed to guarantee prompt vehi…
Autonomous DrivingObjectobject-detectionObject Detection+1Squeezed Convolutional Variational AutoEncoder for Unsupervised Anomaly Detection in Edge Device Industrial Internet of Things
In this paper, we propose Squeezed Convolutional Variational AutoEncoder (SCVAE) for anomaly detection in time series data for Edge Computing in Industrial Internet of Things (IIoT). The proposed model is applied to labe…
Anomaly DetectionEdge-computingTime SeriesTime Series Analysis+1