US 2026/0016593 A1 · MACHINE LEARNING BASED OBJECT DETECTION FOR TRANSIT SYSTEMS

Abstract

Systems and methods for object detection through a fare gate in a transit system is disclosed. The object detection system includes a radar positioned at a first position and a reflective surface positioned at a second position of the fare gate, a machine learning (ML) engine, and a forensic engine. The radar emits a signal and generates a primary clustered point cloud from the signal reflected back from the object in a primary field-of-view (FOV). The reflective surface has a secondary FOV with a secondary clustered point cloud of the object. The ML engine extracts features of the object from the primary clustered point cloud and the secondary clustered point cloud and correlates the features with object profiles. The ML engine further determines that an object profile corresponds with an anomaly and generates a flag. The forensic engine captures media corresponding to the object associated with the flag.

Claims20

Dependent claims

An object detection system to detect an object transiting through a fare gate in a transit system, the object detection system comprises:

a radar positioned at a first position of the fare gate, wherein the radar:
emits a signal, and
generates a primary point cloud from the signal reflected back from the object in a primary field-of-view (FOV);
clustering the primary point cloud to produce a primary clustered point cloud;
a reflective surface positioned at a second position of the fare gate, wherein:
the reflective surface has a secondary FOV, and
the secondary FOV has a secondary clustered point cloud of the object;
a machine learning (ML) engine, wherein the ML engine is operable to:
extract a plurality of features of the object from the primary clustered point cloud and the secondary clustered point cloud,
correlate the plurality of features with a plurality of object profiles, wherein a subset of the plurality of object profiles includes anomalies,
determine an object profile corresponding with an anomaly associated with the object, and
generate a flag upon identifying the anomaly;
and
a forensic engine to capture media corresponding to the object associated with the flag.

11 more elements

An object detection method for detecting an object transiting through a fare gate in a transit system, the object detection method comprises:

positioning a radar at a first position of the fare gate, wherein the radar:
emits a signal, and
generates a primary clustered point cloud from the signal reflected back from the object in a primary field-of-view (FOV);
positioning a reflective surface at a second position of the fare gate, wherein:
the reflective surface has a secondary FOV, and the secondary FOV has a secondary clustered point cloud of the object;
configuring a machine learning (ML) engine to:
extract a plurality of features of the object from the primary clustered point cloud and the secondary clustered point cloud,
correlate the plurality of features with a plurality of object profiles, wherein a subset of the plurality of object profiles includes anomalies,
determine an object profile corresponding with an anomaly associated with the object, and
generate a flag upon identifying the anomaly;
and
capturing media corresponding to the object associated with the flag via a forensic engine.

9 more elements

A machine-readable medium having machine-executable instructions embodied thereon that, when executed by one or more processors, facilitate a method for object detection for detecting an object transiting through a fare gate in a transit system, wherein the method comprises:

positioning a radar at a first position of the fare gate, wherein the radar:
emits a signal, and
generates a primary point cloud from the signal reflected back from the object in a primary field-of-view (FOV);
positioning a reflective surface at a second position of the fare gate, wherein:
the reflective surface has a secondary FOV, and the secondary FOV has a secondary point cloud of the object;
configuring a machine learning (ML) engine to:
extract a plurality of features of the object from the primary point cloud and the secondary point cloud,
correlate the plurality of features with a plurality of object profiles, wherein a subset of the plurality of object profiles includes anomalies,
determine an object profile corresponding with an anomaly associated with the object, and
generate a flag upon identifying the anomaly;
and
capturing media corresponding to the object associated with the flag via a forensic engine.

9 more elements

Dependencies are inferred from the claim text.

Machine learning based object detection for transit systems

Life of this patent

Machine learning based object detection for transit systems

Life of this patent

Abstract

Claims20