System and method for shooter imagery and target shooting analytics

Abstract

An electronic weapon training system is described. The system features one or more targets and a computing device that is configured to concurrently process (i) data associated with shots detected to hit the target to generate metrics associated with these shots and a training session in which the shots occurred and (ii) imagery associated with a shooter captured by a camera integrated as part of the computing device. The shooter imagery is used to identify visual cues that may be used in selection of drills or exercised performed during the training session or during a future training session.

What is claimed is: 
     
         1 . An electronic weapon training system comprising:
 a computing device including one or more cameras; and   an electronic target communicatively coupled to the computing device, the electronic target is configured to (i) detect a plurality of shots provided from a training weapon in which each shot of the plurality of shots corresponds to a light beam coming into contact with a front surface of the electronic target, (ii) compute metrics associated with each shot of the plurality of shots, and (iii) transmit the metrics to the computing device,   wherein the computing device is configured to concurrently process (i) the metrics to generate additional metrics associated with a training session during which the plurality of shots are provided from the training weapon and (ii) imagery of a shooter of the training weapon captured by a first camera of the one or more cameras to identify visual cues that may be used in selection of drills or exercised performed during the training session or during a future training session.   
     
     
         2 . The electronic weapon training system of  claim 1 , wherein the metrics computed for each shot of the plurality of shots, including a second shot of the plurality of shots, include a geographic location of the second shot of the plurality of shots. 
     
     
         3 . The electronic weapon training system of  claim 2 , wherein the metrics computed for each shot of the plurality of shots including the second shot include an identification as to whether the second shot is detected to be positioned within a predetermined region of a silhouette image rendered on the front surface of the electronic target. 
     
     
         4 . The electronic weapon training system of  claim 3 , wherein the metrics computed for each shot of the plurality of shots including the second shot include a timestamp of the second shot identifying a time of contact with the front surface of the electronic target by the light beam corresponding to the second shot. 
     
     
         5 . The electronic weapon training system of  claim 1 , wherein the computing device includes a visual cue analytics logic configured to identify visual cues associated with the imagery of the shooter captured by the first camera by parsing the imagery and conducting analytics on positioning or movement of a body part associated with the shooter against known body part positionings or movements that detrimentally influence shooting accuracy or weapon safety. 
     
     
         6 . The electronic weapon training system of  claim 5 , wherein the visual cue analytics logic operates in combination with one or more machine-learning models in conducting analytics on a visual cue directed to the positioning or movement of the body part associated with the shooter to identify positioning or movement of the body part needs adjustment or calibration to improve shooting accuracy or weapon safety. 
     
     
         7 . An electronic weapon training system comprising:
 a training weapon to emit a light beam to represent a shot fired from the training weapon;   a target made of a light reflecting material or a photoluminescent material; and   a computing device including a plurality of cameras including a first camera and a second camera, a processor, and a non-transitory storage medium including shot analytics logic, visual cue analytics logic, and training session control logic,   wherein the shot analytics logic is configured to receive imagery associated with the target from the first camera, detect a plurality of shots from the training weapon during a current training session in which each shot of the plurality of shots corresponds to a light beam coming into contact with a front surface of the target, and compute metrics associated with each shot of the plurality of shots including a geographic location of each shot,   wherein the visual cue analytics logic is further configured to concurrently receive imagery associated with a shooter of the training weapon captured by second camera and identify visual cues associated with positioning or movement of body parts by the shooter that may be used by the training session control logic to select of drills or exercised performed during the current training session or during a future training session.   
     
     
         8 . The electronic weapon training system of  claim 7 , wherein the metrics computed for each shot of the plurality of shots, including a second shot of the plurality of shots, include the geographic location of the second shot of the plurality of shots. 
     
     
         9 . The electronic weapon training system of  claim 8 , wherein the metrics computed for each shot of the plurality of shots including the second shot include an identification as to whether the second shot is detected to be positioned within a predetermined region of a silhouette image placed on the front surface of the target. 
     
     
         10 . The electronic weapon training system of  claim 9 , wherein the metrics computed for each shot of the plurality of shots including the second shot include a timestamp of the second shot identifying a time of contact with the front surface of the target by the light beam corresponding to the second shot. 
     
     
         11 . The electronic weapon training system of  claim 7 , wherein the visual cue analytics logic of the computing device is configured to operate with one or more machine-learning (ML) models to identify the visual cues associated with the imagery of the shooter captured by the second camera by at least parsing the imagery of the shooter and conducting analytics on positioning or movement of one of more of the body parts associated with the shooter against known body part positionings or movements analyzed by the one or more ML models that detrimentally influence shooting accuracy or weapon safety. 
     
     
         12 . A method for an electronic weapon training system, comprising:
 providing a computing device including one or more cameras;   coupling an electronic target communicatively to the computing device;   configuring the electronic target to (i) detect a plurality of shots provided from a training weapon in which each shot of the plurality of shots corresponds to a light beam coming into contact with a front surface of the electronic target, (ii) compute metrics associated with each shot of the plurality of shots, and (iii) transmit the metrics to the computing device; and   configuring the computing device to concurrently process (i) the metrics to generate additional metrics associated with a training session during which the plurality of shots are provided from the training weapon and (ii) imagery of a shooter of the training weapon captured by a first camera of the one or more cameras to identify visual cues that may be used in selection of drills or exercised performed during the training session or during a future training session.   
     
     
         13 . The method of  claim 12 , wherein configuring the electronic target includes configuring the electronic target to compute the metrics for each shot of the plurality of shots, including a second shot of the plurality of shots, to include a geographic location of the second shot of the plurality of shots. 
     
     
         14 . The method of  claim 13 , wherein configuring the electronic target includes configuring the electronic target to compute the metrics for each shot of the plurality of shots including the second shot to include an identification as to whether the second shot is detected to be positioned within a predetermined region of a silhouette image rendered on the front surface of the electronic target. 
     
     
         15 . The method of  claim 14 , wherein configuring the electronic target includes configuring the electronic target to compute the metrics for each shot of the plurality of shots including the second shot to include a timestamp of the second shot identifying a time of contact with the front surface of the electronic target by the light beam corresponding to the second shot. 
     
     
         16 . The method of  claim 12 , wherein providing the computing device includes configuring a visual cue analytics logic to identify visual cues associated with the imagery of the shooter captured by the first camera by parsing the imagery and conducting analytics on positioning or movement of a body part associated with the shooter against known body part positionings or movements that detrimentally influence shooting accuracy or weapon safety. 
     
     
         17 . The method of  claim 16 , wherein configuring the visual cue analytics logic includes configuring the visual cue analytics logic to operate in combination with one or more machine-learning models in conducting analytics on a visual cue directed to the positioning or movement of the body part associated with the shooter to identify positioning or movement of the body part needs adjustment or calibration to improve shooting accuracy or weapon safety.