Abstract: In various examples, live perception from sensors of an ego-machine may be leveraged to detect objects and assign the objects to bounded regions (e.g., lanes or a roadway) in an environment of the ego-machine in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute outputs—such as output segmentation masks—that may correspond to a combination of object classification and lane identifiers. The output masks may be post-processed to determine object to lane assignments that assign detected objects to lanes in order to aid an autonomous or semi-autonomous machine in a surrounding environment.

Abstract: A method detects an object in a scene by first determining an active set of window positions from depth data. Specifically, the object can be a person. The depth data are acquired by a depth sensor. For each, window position perform the following steps. Assign a window size based on the depth data. Select a current window from the active set of window positions. Extract a joint feature from the depth data and texture data for the current window, wherein the texture data are acquired by a camera. Classify the joint feature to detect the object. The classifier is trained with joint training features extracted from training data including training depth data and training texture data acquired by the sensor and camera respectively. Finally, the active set of window positions is updated before processing the next current window.

Abstract: A method detects an object in a scene by first determining an active set of window positions from depth data. Specifically, the object can be a person. The depth data are acquired by a depth sensor. For each window position perform the following steps. Assign a window size based on the depth data. Select, a current window from the active set of window positions. Extract a joint feature from the depth data and texture data for the current window, wherein the texture data are acquired by a camera. Classify the joint feature to detect the object. The classifier is trained with joint training features extracted from training data including training depth data and training texture data acquired by the sensor and camera respectively. Finally, the active set of windows position is updated before processing the next current window.