… by a moving camera without additional sensors. Moving object detection is relatively difficult
for video captured by a moving camera, since camera motion and object motion are mixed. …

… In this paper a deep neural network is used to do 3D object detection on a radar
pointcloud and a camera image. The network is similar to the network used by Ku et al. [11]. …

… DNN for Object Detection This section reviews the recent development of DNNbased
detectors that are applicable to autonomous driving scenarios. One frame captured by sensors …

… We evaluate all models on held-out camera locations, using established object detection …
Since animals in camera traps occur in groups, crossobject intra-image context is valuable. …

… from stereo vision camera, rectified image frames are taken and 3D cloud is formed via
disparity calculation [3]. Pertained CNN (MobileNetSSD) [7] is used for object detection and …

… One common approach fuses the LIDAR point cloud with the camera image at the pixel-level,
wherein for each LIDAR point there is a corresponding colour-intensity pixel in the image […

… MOVING OBJECT DETECTION The main goal of our vision system is to detect moving
objects in the omnidirectional image while the robot itself moves. However, this proves to be a …

… Frustum PointNet [23] directly operates on the raw point clouds obtained from an RGB-D
camera and uses the RGB image and a 2D object detector to localize objects in the point cloud. …

… We project all the 3D detection candidates into the image plane using the calibration
parameters between LiDAR and camera, and these constitute cues for our 3D-Q-2D image …

… Figure 2: Schematic illustration of our proposed PolarFormer for multi-camera 3D object
detection. For each image captured by any camera view, our model first extracts the feature …