Відео

P: Our network prediction, GT: Ground Truth. This work proposes a Recurrent-CNN-based network for depth estimation using an event camera, a frame camera, and a LiDAR sensor, we evaluate our model on The MVSEC Dataset. GitHub page: github.com/mehdighasemzadeh/Depth_Estimation_Camera_LiDAR_Fusion

P: Our network prediction, GT: Ground Truth. This work proposes a Recurrent-CNN-based network for depth estimation using an event camera, a frame camera, and a LiDAR sensor, we evaluate our model on The MVSEC Dataset. GitHub page: github.com/mehdighasemzadeh/Depth_Estimation_Camera_LiDAR_Fusion

P: Our network prediction, GT: Ground Truth. This work proposes a Recurrent-CNN-based network for depth estimation using an event camera, a frame camera, and a LiDAR sensor, we evaluate our model on The MVSEC Dataset. GitHub page: github.com/mehdighasemzadeh/Depth_Estimation_Camera_LiDAR_Fusion

The robustness test on Event-Scape dataset is done using a wide range of blurred images. A wide range of blurred images are fed to the network, and the network’s predictions are shown in the video, results show our network is robust to the wide degree of blurred images although the accuracy decreases with increasing the blurring, outputs are still reliable. GT: Ground Truth P: Predicted Our Git...

This is a video from the results of our network for crowd counting. Density map of people and the number of people are shown in this video. Github: github.com/mehdighasemzadeh/Crowd-Counting-Density-Based

Crowd Counting using YOLOv5 on Mall Dataset, the number of people and detected heads are shown in this video. Mall Dataset: personal.ie.cuhk.edu.hk/~ccloy/downloads_mall_dataset.html GitHub: github.com/mehdighasemzadeh/Crowd-Counting-YOLOV5.git

This is a video that reveals our network performance on Event-Scape dataset. Event-Scape dataset: github.com/uzh-rpg/rpg_ramnet P: Predicted GT: Ground Truth Our GitHub page: github.com/mehdighasemzadeh/Event-Frame-Based-Semantic-Segmentation.git

This is a video from our network performance on DDD17 dataset which is introduced by Ev-SegNet. DDD17 dataset: sensors.ini.uzh.ch/news_page/DDD17.html Ev-SegNet: github.com/Shathe/Ev-SegNet P: Predicted GT: Ground Truth Our GitHub page: github.com/mehdighasemzadeh/Event-Frame-Based-Semantic-Segmentation.git

This is a video from the results of our network on EventScape dataset EventScape dataset: github.com/uzh-rpg/rpg_ramnet P: Predicted GT: Ground Truth Our GitHub page: github.com/mehdighasemzadeh/Event-Based-Semantic-Segmentation.git

This is a video from the results of our network on DDD17 dataset which was introduced by Ev-SegNet DDD17 dataset: sensors.ini.uzh.ch/news_page/DDD17.html Ev-SegNet: github.com/Shathe/Ev-SegNet P: Predicted GT: Ground Truth Our GitHub page: github.com/mehdighasemzadeh/Event-Based-Semantic-Segmentation.git