How To Have A High Data Acquisition Camera Rate

Effigy 1.

Data recorded by the collision alert device. The chest-mounted video camera captures scene videos, and each video frame is embedded with relevant device data including whether a collision warning was provided, the management of standoff alarm (left, center, right), device operating mode, and the real-time motility sensor information. If a standoff warning is provided, its location is indicated on the video frame (white box with a dot in the center). This helps in determining the object for which the alert was provided. The text information embedded at the summit and bottom of the video frames are extracted past OCR processing, for computerized preprocessing, but they are not visible to study staff in video reviewing.

Effigy two.

Flowchart showing the steps in video data processing to obtain quantifiable mobility outcomes.

Figure three.

Reviewing and coding a standoff alert event. (Left) An event tin can unfold in a complex manner, and depending on how it unfolds and the action taken by the user could effect in contact with the obstacle. Post-obit a complex tree for detailed annotation of an event may not exist viable or possible straight via video review. Success and failure tin can exist either divers from a user's perspective or from device's perspective. From user'due south perspective, non having a body contact can be considered as a success, irrespective of the reason. From device's perspective, a cane contact may be considered a failure even if at that place is no body contact, depending on when the cane contact happens. (Right) Conceptually breaking downwards an issue into three categories: device performance, user action, and the final result, can help to simplify the coding of an event while maintaining thoroughness of the review process.

Effigy iv.

Agreement/disagreement between the two masked reviewers when performing manual review of the video data. A total of 2712 events were reviewed independently by each reviewer (rater A and rater B). The iv review items shown here were rated hierarchically in following order: valid event, truthful hazard, all contacts, and trunk contacts. If both the reviewers rated no for any given particular, the event was dropped from consideration for subsequent review items. Therefore, the total number of events was lower for items lower in the hierarchy.

Effigy 5.

Results for predicting disagreements in rating of body contacts during event review past the two raters. The machine learning algorithm was trained on each reviewer's ratings for the same 2712 events with 200 known disagreements. The % values in the table are relative to the total events reviewed (2712). Results were computed using five-fold cross-validation for these set of events. For data reviewed past rater A, the algorithm predicted 176 disagreements, with rater B correctly, while missing 24 (success charge per unit of 88%). For data reviewed by rater B, the algorithm predicted 185 disagreements with rater A, while missing 15 (success rate of 93%).

Table 1.

Definitions of the Annotation Categories Used to Rate Events

Table 2.

Inter-Rater Reliability Betwixt the ii Contained Reviewers for Ratings of Valid Event, True Gamble, all Contact, and Body Contact Across 2712 Events

Source: https://tvst.arvojournals.org/article.aspx?articleid=2766331

Posted by: comesbeamer.blogspot.com