Disney Research Team Unveils Cutting-Edge Video Surveillance Software
Researchers at Disney have developed a new software that can follow the same subject across a network of surveillance cameras.
PITTSBURGH — Keeping a theme park filled with thousands of people safe is no easy task. Disney theme parks are known for their “Imagineers,” so it should come as no surprise that its researchers have developed a new video surveillance software.
Designed to track faces around the park, the software relies on a ‘deep spatial pyramid’ method for person re-identification.
Here is the abstract from the research paper:
Re-identification refers to the task of finding the same subject across a network of surveillance cameras. This task must deal with appearance changes caused by variations in illumination, a person’s pose, camera viewing angle and background clutter. State-of-the-art approaches usually focus either on feature modeling – designing image descriptors that are robust to changes in imaging conditions, or dissimilarity functions – learning effective metrics to compare images from different cameras.
Typically, with novel deep architectures both approaches can be merged into a single end-to-end training, but to become effective, this requires annotating thousands of subjects in each camera pair. Unlike standard CNN-based approaches, we introduce a spatial pyramid-like structure to the image and learn CNNs for image sub-regions at different scales. When training a CNN using only image sub-regions, we force the model to recognize not only the person’s identity but also the spatial location of the sub-region. This results in highly effective feature representations, which when combined with Mahalanobis-like metric learning significantly outperform state-of-the-art approaches.
You’re probably wondering what this all means. To put it simply, instead of identifying the full face of a person, the video surveillance software only identifies small rectangular sub-regions (patches) of a person’s face.
This allows for easier identification and tracking in poor lighting and background conditions and different camera angles due to the model recognizing not only the person’s identity, but also the spatial location of the sub-region.
After experimenting with the software, the research team found that the deep spatial pyramid software was more effective than previously used detection systems.
While park-goers may appreciate Disney’s focus on improving security, there will no doubt be backlash over privacy concerns.
Concerns over privacy are at an all-time high. Are you comfortable with giving up privacy for more efficient security?
You can view the full Disney research paper here.
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