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公开(公告)号：US10997421B2
公开(公告)日：2021-05-04
申请号：US15947032
申请日：2018-04-06
Applicant: HRL Laboratories, LLC
Inventor： Deepak Khosla , Ryan M. Uhlenbrock , Yang Chen
Abstract: Described is a system for visual activity recognition that includes one or more processors and a memory, the memory being a non-transitory computer-readable medium having executable instructions encoded thereon, such that upon execution of the instructions, the one or more processors perform operations including detecting a set of objects of interest in video data and determining an object classification for each object in the set of objects of interest, the set including at least one object of interest. The one or more processors further perform operations including forming a corresponding activity track for each object in the set of objects of interest by tracking each object across frames. The one or more processors further perform operations including, for each object of interest and using a feature extractor, determining a corresponding feature in the video data. The system may provide a report to a user's cell phone or central monitoring facility.
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2.发明授权公开(公告)号：US10909407B2
公开(公告)日：2021-02-02
申请号：US15934767
申请日：2018-03-23
Applicant: HRL Laboratories, LLC
Inventor： Ryan M. Uhlenbrock , Yang Chen , Deepak Khosla
Abstract: Described is a system for converting a convolutional neural network (CNN) designed and trained for color (RGB) images to one that works on infrared (IR) or grayscale images. The converted CNN comprises a series of convolution layers of neurons arranged in a set kernels having corresponding depth slices. The converted CNN is used for performing object detection. A mechanical component of an autonomous device is controlled based on the object detection.
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3.发明申请公开(公告)号：US20190180119A1
公开(公告)日：2019-06-13
申请号：US16276513
申请日：2019-02-14
Applicant: HRL Laboratories, LLC
Inventor： Yang Chen , Deepak Khosla , Ryan M. Uhlenbrock
Abstract: Described is an object recognition system. Using an integral channel features (ICF) detector, the system extracts a candidate target region (having an associated original confidence score representing a candidate object) from an input image of a scene surrounding a platform. A modified confidence score is generated based on a location and height of detection of the candidate object. The candidate target regions are classified based on the modified confidence score using a trained convolutional neural network (CNN) classifier, resulting in classified objects. The classified objects are tracked using a multi-target tracker for final classification of each classified object as a target or non-target. If the classified object is a target, a device can be controlled based on the target.
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公开(公告)号：US10289910B1
公开(公告)日：2019-05-14
申请号：US15399522
申请日：2017-01-05
Applicant: HRL Laboratories, LLC
Inventor： Yang Chen , Deepak Khosla
Abstract: Described is a system for real-time object recognition. During operation, the system extracts convolutional neural network (CNN) feature vectors from an input image. The input image reflects a scene proximate the system, with the feature vector representing an object in the input image. The CNN feature vector is matched against feature vectors stored in a feature dictionary to identify k nearest neighbors for each object class stored in the feature dictionary. The matching results in a probability distribution over object classes stored in the feature dictionary. The probability distribution provides a confidence score that each of the object classes in the feature dictionary are representative of the object in the input image. Based on the confidence scores, the object in the input image is then recognized as being a particular object class when the confidence score for the particular object class exceeds a threshold.
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公开(公告)号：US10275668B1
公开(公告)日：2019-04-30
申请号：US15271025
申请日：2016-09-20
Applicant: HRL Laboratories, LLC
Inventor： Benjamin Nuernberger , Deepak Khosla , Kyungnam Kim , Yang Chen , Fredy Monterroza
Abstract: Described is a system for collision detection and avoidance estimation using sub-region based optical flow. During operation, the system estimates time-to-contact (TTC) values for an obstacle in multiple regions-of-interest (ROI) in successive image frames as obtained from a monocular camera. Based on the TTC values, the system detects if there is an imminent obstacle. If there is an imminent obstacle, a path for avoiding the obstacle is determined based on the TTC values in the multiple ROI. Finally, a mobile platform is caused to move in the path as determined to avoid the obstacle.
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Patent Agency Ranking
公开(公告)号：US10198689B2
公开(公告)日：2019-02-05
申请号：US15269777
申请日：2016-09-19
Applicant: HRL Laboratories, LLC
Inventor： Yongqiang Cao , Qin Jiang , Yang Chen , Deepak Khosla
Abstract: Described is a system for object detection in images or videos using spiking neural networks. An intensity saliency map is generated from an intensity of an input image having color components using a spiking neural network. Additionally, a color saliency map is generated from a plurality of colors in the input image using a spiking neural network. An object detection model is generated by combining the intensity saliency map and multiple color saliency maps. The object detection model is used to detect multiple objects of interest in the input image.
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公开(公告)号：US20180300553A1
公开(公告)日：2018-10-18
申请号：US15947032
申请日：2018-04-06
Applicant: HRL Laboratories, LLC
Inventor： Deepak Khosla , Ryan M. Uhlenbrock , Yang Chen
Abstract: Described is a system for visual activity recognition that includes one or more processors and a memory, the memory being a non-transitory computer-readable medium having executable instructions encoded thereon, such that upon execution of the instructions, the one or more processors perform operations including detecting a set of objects of interest in video data and determining an object classification for each object in the set of objects of interest, the set including at least one object of interest. The one or more processors further perform operations including forming a corresponding activity track for each object in the set of objects of interest by tracking each object across frames. The one or more processors further perform operations including, for each object of interest and using a feature extractor, determining a corresponding feature in the video data. The system may provide a report to a user's cell phone or central monitoring facility.
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公开(公告)号：US09894285B1
公开(公告)日：2018-02-13
申请号：US14972033
申请日：2015-12-16
Applicant: HRL Laboratories, LLC
Inventor： Zhichao Chen , Yang Chen , Deepak Khosla , Darrel J. VanBuer
CPC classification number: H04N5/2353 , G06T5/20 , G06T7/0081 , G06T2207/20021 , G06T2207/20104 , H04N5/243 , H04N5/3355
Abstract: Described is system for real-time automated exposure adjustment of a camera using contrast entropy. Multiple images are captured using an image sensor, where each of the images is captured at a distinct exposure value. A contrast entropy is determined for each of the multiple images. An image having the largest contrast entropy is selected among the images and output.
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9.发明授权公开(公告)号：US09342759B1
公开(公告)日：2016-05-17
申请号：US14204384
申请日：2014-03-11
Applicant: HRL Laboratories, LLC
Inventor： Yang Chen , Changsoo S. Jeong , Deepak Khosla , Kyungnam Kim , Shinko Y. Cheng , Lei Zhang , Alexander L. Honda
IPC: G06K9/62
CPC classification number: G06K9/6262 , G06K9/00805 , G06K9/32 , G06K2009/3291 , G06T7/248
Abstract: Described is a system for improving object recognition. Object detection results and classification results for a sequence of image frames are received as input. Each object detection result is represented by a detection box and each classification result is represented by an object label corresponding to the object detection result. A pseudo-tracklet is formed by linking object detection results representing the same object in consecutive image frames. The system determines whether there are any inconsistent object labels or missing object detection results in the pseudo-tracklet. Finally, the object detection results and the classification results are improved by correcting any inconsistent object labels and missing object detection results.
Abstract translation: 描述了一种改善对象识别的系统。 接收作为输入的图像帧序列的对象检测结果和分类结果。 每个对象检测结果由检测框表示，并且每个分类结果由与对象检测结果对应的对象标签表示。 通过将表示相同对象的对象检测结果链接在连续图像帧中来形成伪轨迹。 系统确定伪踪迹中是否存在任何不一致的对象标签或丢失的对象检测结果。 最后，通过校正任何不一致的对象标签和缺少的对象检测结果来改进对象检测结果和分类结果。
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10.发明授权Selective color processing for vision systems that enables optimal detection and recognition 有权视觉系统的选择性颜色处理，可实现最佳的检测和识别公开(公告)号：US09111355B1
公开(公告)日：2015-08-18
申请号：US14205327
申请日：2014-03-11
Applicant: HRL Laboratories, LLC
Inventor： Alexander L Honda , Yang Chen , Shinko Y. Cheng , Deepak Khosla , Kyungnam Kim , Changsoo S. Jeong , Lei Zhang
CPC classification number: G06T5/008 , G06T2207/10024
Abstract: Described is a system for selective color processing for vision systems. The system receives a multi-band image as input. As an optional step, the multi-band image is preprocessed, and a transformation is performed to transform the multi-band image into a color space. A metric function is applied to the transformed image to generate a distance map comprising intensities which vary based on a similarity between an intensity of a pixel color and an intensity of a target color. A contrast enhancement process is applied to the distance map to normalize the distance map to a range of values. The range of values is expanded near the intensity of the target color. Finally, an output response map for the target color of interest is generated, such that the output response map has high responses in regions which are similar to the target color to aid in detection and recognition processes.
Abstract translation: 描述了用于视觉系统的选择性颜色处理的系统。 系统接收多频带图像作为输入。 作为可选步骤，对多频带图像进行预处理，并且进行变换以将多频带图像变换为颜色空间。 度量函数被应用于变换图像以产生距离图，其包括基于像素颜色的强度和目标颜色的强度之间的相似度而变化的强度。 将对比度增强过程应用于距离图，以将距离图归一化为一定范围的值。 值的范围在目标颜色的强度附近扩展。 最后，生成目标感兴趣的颜色的输出响应图，使得输出响应图在与目标颜色相似的区域中具有高响应，以帮助检测和识别过程。
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