The following examples are taken from the Capability pages. They have been collected here for convenience purposes only. For more information, navigate to the specific capabity using the icons.
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Object Recognition |
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1. Recognition of the four playing card suits (hearts, clubs, diamonds, and spades). The suits are hand drawn on a piece of paper to show that the technology is not bound to any particular style. MPEG (3.1 MB) |
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2. Recognition of characters on a license plate being held in outside in front of a window. It should be noted that the technology is not searching for pre-defined symbols (as per template matching), it is actually recognising the characters. The use of a license plate is arbitrary - it could be a sign on a wall, markings on a ship, name badge on a chest, etc. MPEG (3.9 MB) |
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Object Tracking - Dynamic Knowledge Base |
These videos illustrate the use of Deep Vision's sensor exploitation technology with a dynamic knowledge base. The user selects and labels an object from within the scene. The description and label pair is added to the knowledge base. The memory requirements of the expression (e.g. to transmit and store) are appended to the label for clarity. The overall size of the knowledge base is indicated at the top-right corner along with the memory requirements of all recognised objects.
The demonstration software operates within the 33 ms imposed by the attached camera, resulting in fluid object recognition and tracking. The current frame rate, which includes both processing and rendering, is displayed in the bottom-right corner.
It should be noted that multiple occurrences of these objects of interest could have been placed throughout the environment. Each one would have been found and recognised. However, for clarity, only one instance of each object of interest is presented.
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1. The user selects two objects: a cross and the letter H. Each object is given a meaningfull label: First Aid and Hospital respectively. These objects are then placed in the environment and the camera is moved within it. Each object presented on the sensor and found in the knowledge base is highlighted and labelled accordingly. MPEG (29 MB) OGG Theora (12.8 MB) |
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2. The users selects three objects: the letter H, an airplane and a cross. Each object is given a meaningful label: Hospital, Airport and First Aid respectively. These objects are then moved throughout the environment. MPEG (32.4 MB) OGG Theora (13.4 MB) |
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Object Tracking - Motion Analysis |
These videos illustrate the use of Deep Vision's sensor exploitation technology with low-level motion analysis.
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1. Simultaneous tracking of two people in motion (delineated by bounding box). MPEG (1 MB) OGG Theora (851 KB) |
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2. Tracking of an intermittently moving person. The person remains locked even when motionless (indicated by the constant colour of the bounding box). Note that the pickup truck near the end of the sequence (top right corner) is ignored, demonstrating the ability to differentiate between items of interest in any scene. MPEG (1.3 MB) OGG Theora (880 KB) |
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3. Preferential tracking of a moving person. The moving vehicle is ignored in favour of the person even after the person has left the scene. MPEG (760 KB) OGG Theora (596 KB) |
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Passive Ranging |
The following videos illustrate the use of Deep Vision's sensor exploitation technology for passive ranging. This technology is capable of determining an object of interest's bearing relative to the sensor. The bearing information includes:
- Line-of-sight distance between the object and the centre of the sensor
- Distance between the object and the centre of the sensor in all three planes: Horizontal (X-axis), Vertical (Y-axis) and Depth (Z-axis)
- Velocity of the object (relative to the sensor)
- Acceleration of the object (relative to the sensor)
- Azimuth of the object (relative to the sensor)
- Elevation of the object (relative to the sensor)
Additional navigational information can be derived from the above. These videos used a single, static knowledge base consisting of the following description-label pairs:
- A cross: First Aid
- An airplane: Airport
- The letter H: Hospital
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1. This video illustrates the recognition of the First Aid object. The bearing information and associated label of the object is displayed at the top. The 3D distance of the object, relative to the sensor, is presented in a tooltip attached to the object. MPEG (15.7 MB) OGG Theora (6.2 MB) |
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2. This video uses the same raw video used in the Object Tracking video above. In this video, the First Aid and Hospital objects are recognised and their bearing information is displayed. MPEG (23.7 MB) OGG Theora (4.9 MB) |
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3. In this video the distance between two objects is displayed. When two or more recognised objects are present, the user must select the desired object in order to see the bearing information. In actuality, the bearing information for every recognised object is known - the selection mechanism is only a means of presentation. MPEG (15.2 MB) OGG Theora (4.9 MB) |
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4. This video demonstrates how the technology could be used for interactive navigation. The application guides the user through a series of signs by evaluating the user's movements and describing any errors. The following capabilities are illustrated:
Though this scenario is fictitious, the results shown are real. The navigation application has been created and is currently in use in the demo system. |
For your convenience, the above videos are available in both MPEG and OGG Theora formats. The MPEG videos should be viewable by any modern media player. The OGG Theora videos are playable by any Linux distribution. Microsoft Windows users will need to download and install the OGG codec. It is recommended that the OGG Theora videos be viewed as they offer superior quality and file size.
