https://github.com/RajkumarPreetham/Road-scene-understanding
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Crowd Counting is a task to count people in image. It is mainly used in real-life for automated public monitoring such as surveillance and traffic control. Different from object detection, Crowd Counting aims at recognizing arbitrarily sized targets in various situations including sparse and cluttering scenes at the same time.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.