Abstract: A computer-implemented method, system, and computer program product are provided for object detection utilizing an online flow guided memory network. The method includes receiving a plurality of videos, each of the plurality of videos including a plurality of frames. The method also includes generating, with a feature extraction network, a frame feature map for a current frame of the plurality of frames. The method additionally includes aggregating a memory feature map from the frame feature map and previous memory feature maps from previous frames on a plurality of time axes, with the plurality of time axes including a first time axis at a first frame increment and a second time axis at a second frame increment. The method further includes predicting, with a task network, an object from the memory feature map. The method also includes controlling an operation of a processor-based machine to react in accordance with the object.

Abstract: Systems and methods for predicting vehicle behavior includes capturing images of a vehicle in traffic using an imaging device. Future behavior of the vehicle is stochastically modeled using a processing device including an energy-based model stored in a memory of the processing device. The energy-based model includes generating a distribution of possible future trajectories of the vehicle using a generator, sampling the distribution of possible future trajectories according to an energy value of each trajectory in the distribution of possible future trajectories an energy model to determine probable future trajectories, and optimizing parameters of each of the generator and the energy model using an optimizer. A user is audibly alerted with a speaker upon an alert system recognizing hazardous trajectories of the probable future trajectories.

Abstract: A computer-implemented method, system, and computer program product are provided for object detection utilizing an online flow guided memory network. The method includes receiving, by a processor, a plurality of videos, each of the plurality of videos including a plurality of frames. The method also includes generating, by the processor with a feature extraction network, a frame feature map for a current frame of the plurality of frames. The method additionally includes determining, by the processor, a memory feature map from the frame feature map and a previous memory feature map from a previous frame by warping the previous memory feature map. The method further includes predicting, by the processor with a task network, an object from the memory feature map. The method also includes controlling an operation of a processor-based machine to react in accordance with the object.

Abstract: Systems and methods for predicting vehicle behavior includes capturing images of a vehicle in traffic using an imaging device. Future behavior of the vehicle is stochastically modeled using a processing device including an energy-based model stored in a memory of the processing device. The energy-based model includes generating a distribution of possible future trajectories of the vehicle using a generator, sampling the distribution of possible future trajectories according to an energy value of each trajectory in the distribution of possible future trajectories an energy model to determine probable future trajectories, and optimizing parameters of each of the generator and the energy model using an optimizer. A user is audibly alerted with a speaker upon an alert system recognizing hazardous trajectories of the probable future trajectories.

Abstract: Methods and systems for predicting a trajectory include determining prediction samples for agents in a scene based on a past trajectory. The prediction samples are ranked according to a likelihood score that incorporates interactions between agents and semantic scene context. The prediction samples are iteratively refined using a regression function that accumulates scene context and agent interactions across iterations. A response activity is triggered when the prediction samples satisfy a predetermined condition.

Abstract: A system and method are provided for driving assistance. The system includes an image capture device configured to capture a video sequence, relative to an outward view from a vehicle, which includes a set of objects and is formed from a set of image frames. The system includes a processor configured to detect the objects to form a set of object detections, and track the set of object detections over the frames to form tracked detections. The processor is configured to generate for a current frame, responsive to conditions, a set of sparse object proposals for a current location of an object based on: (i) the tracked detections of the object from an immediately previous frame; and (ii) detection proposals for the object derived from the current frame. The processor is configured to perform an action to mitigate a likelihood of potential harmful due to a current object location.

Abstract: A system is provided for unsupervised cross-domain image generation relative to a first and second image domain that each include real images. A first generator generates synthetic images similar to real images in the second domain while including a semantic content of real images in the first domain. A second generator generates synthetic images similar to real images in the first domain while including a semantic content of real images in the second domain. A first discriminator discriminates real images in the first domain against synthetic images generated by the second generator. A second discriminator discriminates real images in the second domain against synthetic images generated by the first generator. The discriminators and generators are deep neural networks and respectively form a generative network and a discriminative network in a cyclic GAN framework configured to increase an error rate of the discriminative network to improve synthetic image quality.

Abstract: A surveillance system and method are provided. The surveillance system includes at least one camera configured to capture a set of images of a given target area that includes a set of objects to be tracked. The surveillance system includes a memory storing a learning model configured to perform multi-object tracking by jointly learning arbitrarily parameterized and differentiable cost functions for all variables in a linear program that associates object detections with bounding boxes to form trajectories. The surveillance system includes a processor configured to perform surveillance of the target area to (i) detect the objects and track locations of the objects by applying the learning model to the images in a surveillance task that uses the multi-object tracking, and (ii), provide a listing of the objects and their locations for surveillance task. A bi-level optimization is used to minimize a loss defined on a solution of the linear program.

Abstract: A system and method are provided. The system includes an image capture device configured to capture a video sequence formed from a set of input image frames and including a set of objects. The system further includes a processor configured to detect the objects to form object detections and track the object detections over the input frames to form tracked detections. The processor is also configured to generate for a current input frame, responsive to conditions, a set of sparse object proposals for a current location of an object based on: (i) the tracked detections of the object from an immediately previous input frame; and (ii) detection proposals for the object derived from the current frame. The processor is additionally configured to provide a user perceptible indication of the current location of the object, based on the set of sparse object proposals.

Abstract: An action recognition system and method are provided. The system includes an image capture device configured to capture a video sequence formed from image frames and depicting a set of objects. The system includes a processor configured to detect the objects to form object detections. The processor is configured to track the object detections over the frames to form tracked detections. The processor is configured to generate for a current frame, responsive to conditions, sparse object proposals for a current location of an object based on: (i) the tracked detections of the object from an immediately previous frame; and (ii) detection proposals for the object derived from the current frame. The processor is configured to control a hardware device to perform a response action in response to an identification of an action type of an action performed by the object, the identification being based on the sparse object proposals.

Abstract: A multi-object tracking system and method are provided. The multi-object tracking system includes at least one camera configured to capture a set of input images of a set of objects to be tracked. The multi-object tracking system further includes a memory storing a learning model configured to perform multi-object tracking by jointly learning arbitrarily parameterized and differentiable cost functions for all variables in a linear program that associates object detections with bounding boxes to form trajectories. The multi-object tracking system also includes a processor configured to (i) detect the objects and track locations of the objects by applying the learning model to the set of input images in a multi-object tracking task, and (ii), provide a listing of the objects and the locations of the objects for the multi-object tracking task. A bi-level optimization is used to minimize a loss defined on a solution of the linear program.

Abstract: A surveillance system and method are provided. The surveillance system includes an image capture device configured to capture a video sequence of a target area that includes a set of objects and is formed from a set of image frames. The surveillance system also includes a processor. The processor is configured to detect the objects to form object detections, and track the object detections over the frames to form tracked detections. The processor is further configured to generate for a current input frame, responsive to conditions, a set of sparse object proposals for a current location of an object based on: (i) the tracked detections of the object from an immediately previous frame; and (ii) detection proposals for the object derived from the current frame. The processor is additionally configured to provide a user perceptible indication of the current location of the object, based on the sparse object proposals.

Abstract: A smuggling detection system and corresponding method are provided. The smuggling detection system includes a camera configured to capture an input image of a subject purported to be a baby. The smuggling detection system further includes a memory storing a deep learning model configured to perform a baby detection task for a smuggling detection application. The smuggling detection system also includes a processor configured to apply the deep learning model to the input image to provide a baby detection result of either a presence or an absence of an actual baby in relation to the subject purported to be the baby. The baby detection task is configured to evaluate one or more different distractor modalities corresponding to one or more different physical spoofing materials to prevent baby spoofing for the baby detection task.

Abstract: A mass transit surveillance system and corresponding method are provided. The mass transit surveillance system includes a camera configured to capture an input image of a subject purported to be a baby and presented at a mass transit environment. The mass transit surveillance system further includes a memory storing a deep learning model configured to perform a baby detection task for the mass transit environment. The mass transit surveillance system also includes a processor configured to apply the deep learning model to the input image to provide a baby detection result of either a presence or an absence of an actual baby in relation to the subject purported to be the baby. The baby detection task is configured to evaluate one or more different distractor modalities corresponding to one or more different physical spoofing materials to prevent baby spoofing for the baby detection task.

Abstract: A computer-implemented method, system, and computer program product are provided for object detection utilizing an online flow guided memory network. The method includes receiving, by a processor, a plurality of videos, each of the plurality of videos including a plurality of frames. The method also includes generating, by the processor with a feature extraction network, a frame feature map for a current frame of the plurality of frames. The method additionally includes determining, by the processor, a memory feature map from the frame feature map and a previous memory feature map from a previous frame by warping the previous memory feature map. The method further includes predicting, by the processor with a task network, an object from the memory feature map. The method also includes controlling an operation of a processor-based machine to react in accordance with the object.

Abstract: A computer-implemented method, system, and computer program product are provided for object detection utilizing an online flow guided memory network. The method includes receiving a plurality of videos, each of the plurality of videos including a plurality of frames. The method also includes generating, with a feature extraction network, a frame feature map for a current frame of the plurality of frames. The method additionally includes aggregating a memory feature map from the frame feature map and previous memory feature maps from previous frames on a plurality of time axes, with the plurality of time axes including a first time axis at a first frame increment and a second time axis at a second frame increment. The method further includes predicting, with a task network, an object from the memory feature map. The method also includes controlling an operation of a processor-based machine to react in accordance with the object.

Abstract: Systems and methods for predicting vehicle behavior includes capturing images of a vehicle in traffic using an imaging device. Future behavior of the vehicle is stochastically modeled using a processing device including an energy-based model stored in a memory of the processing device. The energy-based model includes generating a distribution of possible future trajectories of the vehicle using a generator, sampling the distribution of possible future trajectories according to an energy value of each trajectory in the distribution of possible future trajectories an energy model to determine probable future trajectories, and optimizing parameters of each of the generator and the energy model using an optimizer. A user is audibly alerted with a speaker upon an alert system recognizing hazardous trajectories of the probable future trajectories.

Abstract: Systems and methods for predicting vehicle behavior includes capturing images of a vehicle in traffic using an imaging device. Future behavior of the vehicle is stochastically modeled using a processing device including an energy-based model stored in a memory of the processing device. The energy-based model includes generating a distribution of possible future trajectories of the vehicle using a generator, sampling the distribution of possible future trajectories according to an energy value of each trajectory in the distribution of possible future trajectories an energy model to determine probable future trajectories, and optimizing parameters of each of the generator and the energy model using an optimizer. A user is audibly alerted with a speaker upon an alert system recognizing hazardous trajectories of the probable future trajectories.

Abstract: A system is provided for unsupervised cross-domain image generation relative to a first and second image domain that each include real images. A first generator generates synthetic images similar to real images in the second domain while including a semantic content of real images in the first domain. A second generator generates synthetic images similar to real images in the first domain while including a semantic content of real images in the second domain. A first discriminator discriminates real images in the first domain against synthetic images generated by the second generator. A second discriminator discriminates real images in the second domain against synthetic images generated by the first generator. The discriminators and generators are deep neural networks and respectively form a generative network and a discriminative network in a cyclic GAN framework configured to increase an error rate of the discriminative network to improve synthetic image quality.

Abstract: A computer-implemented method executed by at least one processor for training fast models for real-time object detection with knowledge transfer is presented. The method includes employing a Faster Region-based Convolutional Neural Network (R-CNN) as an objection detection framework for performing the real-time object detection, inputting a plurality of images into the Faster R-CNN, and training the Faster R-CNN by learning a student model from a teacher model by employing a weighted cross-entropy loss layer for classification accounting for an imbalance between background classes and object classes, employing a boundary loss layer to enable transfer of knowledge of bounding box regression from the teacher model to the student model, and employing a confidence-weighted binary activation loss layer to train intermediate layers of the student model to achieve similar distribution of neurons as achieved by the teacher model.