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    LIDAR-BASED OBJECT DETECTION AND CLASSIFICATION
    1.
    发明申请
    LIDAR-BASED OBJECT DETECTION AND CLASSIFICATION 审中-公开

    公开(公告)号：US20190049560A1

    公开(公告)日：2019-02-14

    申请号：US16103674

    申请日：2018-08-14

    申请人： Rita Chattopadhyay ,  Monica Lucia Martinez-Canales

    发明人： Rita Chattopadhyay ,  Monica Lucia Martinez-Canales

    IPC分类号： G01S7/48 ,  G06N99/00 ,  G01S17/42

    摘要： Various systems and methods for implementing LiDAR-based object detection and classification are described herein. An object detection system includes a feature extraction and object identification (FEOI) circuit to: receive segmented data of an environment around the object detection system, the segmented data obtained using a light imaging detection and ranging (LiDAR) system, oriented with respect to a direction of travel; compute spatial and structural parameters of a segment of the segmented data; and use the spatial and structural parameters with a machine learning model to obtain a classification of the segment.

    Video based monitoring system and method
    4.
    发明申请
    Video based monitoring system and method 审中-公开
    视频监控系统及方法

    公开(公告)号：US20080181457A1

    公开(公告)日：2008-07-31

    申请号：US12009313

    申请日：2008-01-17

    申请人： Rita Chattopadhyay ,  Archana Kalyansundar

    发明人： Rita Chattopadhyay ,  Archana Kalyansundar

    IPC分类号： G06K9/00

    CPC分类号： G06K9/38 ,  G06K9/00771

    摘要： There is described a video based monitoring system and method. The system comprises an image acquisition module, a movement detection module, and a stationary object detection module. The movement detection module is adapted for detecting the presence of a moving object in a region of interest of said captured video image. The stationary object detection module is adapted for detecting the presence of a stationary object in said region of interest and operable when said movement detection module fails to detect a moving object in a region of interest of a current frame of the captured image. The stationary object detection module includes a pixel-by-pixel comparison module adapted to determine the number of pixels in the region of interest in the current frame whose pixel values match with that of corresponding pixels in an immediately preceding frame. The stationary object detection module further includes a background identification module adapted to identify those pixels in the region of interest in the current frame that form part of a background, based upon a comparison of their pixel values with a background pixel value. The system further includes means for generating a signal to indicate detection of a stationary object when the number of matches between the current frame and the immediately preceding frame exceeds a threshold value after discounting those pixels in the current frame that are identified to be part of the background.

    摘要翻译： 描述了基于视频的监控系统和方法。 该系统包括图像采集模块，移动检测模块和静止物体检测模块。 所述运动检测模块适于检测所述拍摄视频图像的感兴趣区域中的移动物体的存在。 固定物体检测模块适于在所述感兴趣区域中检测静止物体的存在，并且当所述移动检测模块未能检测到所捕获图像的当前帧的感兴趣区域中的移动物体时可操作。 固定物体检测模块包括逐像素比较模块，其适于确定当前帧中的像素值与紧邻的前一帧中的相应像素的像素值相匹配的感兴趣区域中的像素数。 固定物体检测模块还包括背景识别模块，其适于基于其像素值与背景像素值的比较来识别形成背景的一部分的当前帧中的感兴趣区域中的那些像素。 所述系统还包括用于当折扣当前帧中的当前帧和前一帧之间的匹配数目超过阈值时产生用于指示静止对象的检测的信号的装置，所述当前帧中的那些像素被识别为是 背景。

    OPTIMIZED DECISION TREE MACHINE LEARNING FOR RESOURCE-CONSTRAINED DEVICES
    6.
    发明申请
    OPTIMIZED DECISION TREE MACHINE LEARNING FOR RESOURCE-CONSTRAINED DEVICES 审中-公开

    公开(公告)号：US20200311559A1

    公开(公告)日：2020-10-01

    申请号：US16902063

    申请日：2020-06-15

    申请人： Rita Chattopadhyay ,  Rajesh Bansal ,  Yuming Ma ,  Mrittika Ganguli

    发明人： Rita Chattopadhyay ,  Rajesh Bansal ,  Yuming Ma ,  Mrittika Ganguli

    IPC分类号： G06N5/00 ,  G06N5/04 ,  G06N20/00

    摘要： In one embodiment, an edge computing device for performing decision tree training and inference includes interface circuitry and processing circuitry. The interface circuitry receives training data and inference data that is captured, at least partially, by sensor(s). The training data corresponds to a plurality of labeled instances of a feature set, and the inference data corresponds to an unlabeled instance of the feature set. The processing circuitry: computes a set of feature value checkpoints that indicate, for each feature of the feature set, a subset of potential feature values to be evaluated for splitting tree nodes of a decision tree model; trains the decision tree model based on the training data and the set of feature value checkpoints; and performs inference using the decision tree model to predict a target variable for the unlabeled instance of the feature set.