A fast object detection method based on deformable part model (DPM) is provided. The method includes importing a trained classifier for object detection, receiving an image frame from a plurality of frames in a video captured by a camera, and identifying regions possibly containing at least one object via objectness measure based on Binarized Normed Gradients (BING). The method also includes calculating Histogram of Oriented Gradients (HOG) feature pyramid of the image frame, performing DPM detection for the identified regions possibly containing the at least one object, and labeling the at least one detected object using at least one rectangle box via non-maximum suppression (NMS). Further, the method includes processing a next frame from the plurality of frames in the captured video until the video ends and outputting object detection results.

Lossy or lossless compression and transmission, comprising the steps of: (i) receiving an input image; (ii) encoding it to produce a y latent representation; (iii) encoding the y latent representation to produce a z hyperlatent representation; (iv) quantizing the z hyperlatent representation to produce a quantized z hyperlatent representation; (v) entropy encoding the quantized z hyperlatent representation into a first bitstream, (vi) processing the quantized z hyperlatent representation to obtain a location entropy parameter μy, an entropy scale parameter σy, and a context matrix Ay of the y latent representation; (vii) processing the y latent representation, the location entropy parameter μy and the context matrix Ay, to obtain quantized latent residuals; (viii) entropy encoding the quantized latent residuals into a second bitstream; and (ix) transmitting the bitstreams.

Lossy or lossless compression and transmission, comprising the steps of: (i) receiving an input image; (ii) encoding it to produce a y latent representation; (iii) encoding the y latent representation to produce a z hyperlatent representation; (iv) quantizing the z hyperlatent representation to produce a quantized z hyperlatent representation; (v) entropy encoding the quantized z hyperlatent representation into a first bitstream, (vi) processing the quantized z hyperlatent representation to obtain a location entropy parameter μy, an entropy scale parameter σy, and a context matrix Ay of the y latent representation; (vii) processing the y latent representation, the location entropy parameter μy and the context matrix Ay, to obtain quantized latent residuals; (viii) entropy encoding the quantized latent residuals into a second bitstream; and (ix) transmitting the bitstreams.

A model generation system includes: an extraction unit that extracts an object area part, which is an area occupied by an object, from a target image; and a generation unit that performs machine learning by inputting the object area part and that generates a shape classification model for classifying a shape of the object. The use of the shape classification model generated in this manner makes it possible to properly recognize the shape of the object in the image.

A model generation system includes: an extraction unit that extracts an object area part, which is an area occupied by an object, from a target image; and a generation unit that performs machine learning by inputting the object area part and that generates a shape classification model for classifying a shape of the object. The use of the shape classification model generated in this manner makes it possible to properly recognize the shape of the object in the image.

Aspects of the present disclosure relate to personal protective equipment (PPE) management. A set of personal protective equipment (PPE) data describing use time limits of respective PPE articles of a set of PPE articles can be received. Use of a PPE article of the set of PPE articles can be monitored using one or more sensors. A determination can be made whether a PPE usage rule of the PPE article is satisfied based on the monitoring, where the PPE usage rule is based on at least a use time limit of the PPE article. A PPE recommendation action can be issued in response to determining that the PPE usage rule of the PPE article is satisfied.

Aspects of the present disclosure relate to personal protective equipment (PPE) management. A set of personal protective equipment (PPE) data describing use time limits of respective PPE articles of a set of PPE articles can be received. Use of a PPE article of the set of PPE articles can be monitored using one or more sensors. A determination can be made whether a PPE usage rule of the PPE article is satisfied based on the monitoring, where the PPE usage rule is based on at least a use time limit of the PPE article. A PPE recommendation action can be issued in response to determining that the PPE usage rule of the PPE article is satisfied.

Systems, and method and computer readable media that store instructions for generating a hybrid representation of a media unit.

Provided is an apparatus for recognizing user postures. The apparatus recognizes detailed postures such as a stand posture, a bending forward posture, a bending knees posture, a tilt right posture, and a tilt left posture, based on a variation between body information at a previous time and body information at a current time.

Lossy or lossless compression and transmission, comprising the steps of: (i) receiving an input image; (ii) encoding it using an encoder trained neural network, to produce a y latent representation; (iii) encoding the y latent representation using a hyperencoder trained neural network, to produce a z hyperlatent representation; (iv) quantizing the z hyperlatent representation using a predetermined entropy parameter to produce a quantized z hyperlatent representation; (v) entropy encoding the quantized z hyperlatent representation into a first bitstream, using predetermined entropy parameters; (vi) processing the quantized z hyperlatent representation using a hyperdecoder trained neural network to obtain a location entropy parameter μ.sub.y, an entropy scale parameter σ.sub.y, and a context matrix A.sub.y of the y latent representation; (vii) processing the y latent representation, the location entropy parameter μ.sub.y and the context matrix A.sub.y, to obtain quantized latent residuals; (viii) entropy encoding the quantized latent residuals into a second bitstream, using the entropy scale parameter σ.sub.y; and (ix) transmitting the bitstreams.