A method of saving image data of a camera in an accident data recorder of a vehicle involves: a) performing object recognition to determine object data regarding objects detected from the camera image data; b) compressing the camera image data by a lossy compression method to form compressed image data; c) storing the object data and the compressed image data in a storage unit; d) overwriting the data in the storage unit after a predefined volume of data has been stored; and e) statically saving the object data and the compressed image data in response to a trigger signal.

A method of saving image data of a camera in an accident data recorder of a vehicle involves: a) performing object recognition to determine object data regarding objects detected from the camera image data; b) compressing the camera image data by a lossy compression method to form compressed image data; c) storing the object data and the compressed image data in a storage unit; d) overwriting the data in the storage unit after a predefined volume of data has been stored; and e) statically saving the object data and the compressed image data in response to a trigger signal.

An optical system for a vehicle may be configured with a plurality of camera sensors. Each camera sensor may be configured to create respective image data of a respective field of view. The optical system is further configured with a plurality of image processing units coupled to the plurality of camera sensors. The image processing units are configured to compress the image data captured by the camera sensors. A computing system is configured to store the compressed image data in a memory. The computing system is further configured with a vehicle-control processor configured to control the vehicle based on the compressed image data. The optical system and the computing system can be communicatively coupled by a data bus.

An optical system for a vehicle may be configured with a plurality of camera sensors. Each camera sensor may be configured to create respective image data of a respective field of view. The optical system is further configured with a plurality of image processing units coupled to the plurality of camera sensors. The image processing units are configured to compress the image data captured by the camera sensors. A computing system is configured to store the compressed image data in a memory. The computing system is further configured with a vehicle-control processor configured to control the vehicle based on the compressed image data. The optical system and the computing system can be communicatively coupled by a data bus.

A first analysis unit (202) acquires second image data. The second image data is generated by compressing first image data by a predetermined encoding method. Further, the first analysis unit (202) performs image analysis of the acquired second image data to detect second image data satisfying a first predetermined condition. A decoding unit (204) decodes second image data detected by the first analysis unit (202) into third image data having higher resolution than that of the second image data. In the second analysis (206), the image analysis of the third image data is performed.

A first analysis unit (202) acquires second image data. The second image data is generated by compressing first image data by a predetermined encoding method. Further, the first analysis unit (202) performs image analysis of the acquired second image data to detect second image data satisfying a first predetermined condition. A decoding unit (204) decodes second image data detected by the first analysis unit (202) into third image data having higher resolution than that of the second image data. In the second analysis (206), the image analysis of the third image data is performed.

A recompressed-sensing recording means, apparatus, device, or system captures one or more recordings, of possibly unknown or unbounded duration, into a finite memory, by resparsifying previously recorded sensor data in order to make room to store new incoming sensor data. In some embodiments this resparsification is recursive, resulting in a fraccular (fractally circular) buffer. In some embodiments a LightSpaceTimeLapse image capture means, apparatus, or system captures the passage or stoppage of time, by way of analyzing a scene or subject matter that is subject to changes in lighting or changes in the subject matter, or both. In some embodiments a sparse or reduced resolution test image is captured periodically, and a LightSpaceTime model is constructed to estimate changes in LightSpace or SpaceTime or both. Successive frames feed into a LightSpaceTime comparator which triggers full resolution capture into a finite memory at appropriate intervals. As the finite memory capacity approaches full capacity, the image repository is resparsified to make room for more new images. This resparsification is done by a decision process based on LightSpace and SpaceTime analysis. In some embodiments an intermediate LightSpaceTime format is captured and rendered as a background task resulting in an optimization that varies over time, depending on what is considered important in the LightSpaceTime continuum. All exposure and time values are preserved allowing an interpolated reconstruction at constant framerates or constant noveltyrates, as may be desired for artistic or epistemological purposes or for forensically accurate and irrefutable evidence.

A recompressed-sensing recording means, apparatus, device, or system captures one or more recordings, of possibly unknown or unbounded duration, into a finite memory, by resparsifying previously recorded sensor data in order to make room to store new incoming sensor data. In some embodiments this resparsification is recursive, resulting in a fraccular (fractally circular) buffer. In some embodiments a LightSpaceTimeLapse image capture means, apparatus, or system captures the passage or stoppage of time, by way of analyzing a scene or subject matter that is subject to changes in lighting or changes in the subject matter, or both. In some embodiments a sparse or reduced resolution test image is captured periodically, and a LightSpaceTime model is constructed to estimate changes in LightSpace or SpaceTime or both. Successive frames feed into a LightSpaceTime comparator which triggers full resolution capture into a finite memory at appropriate intervals. As the finite memory capacity approaches full capacity, the image repository is resparsified to make room for more new images. This resparsification is done by a decision process based on LightSpace and SpaceTime analysis. In some embodiments an intermediate LightSpaceTime format is captured and rendered as a background task resulting in an optimization that varies over time, depending on what is considered important in the LightSpaceTime continuum. All exposure and time values are preserved allowing an interpolated reconstruction at constant framerates or constant noveltyrates, as may be desired for artistic or epistemological purposes or for forensically accurate and irrefutable evidence.

A video headband assembly, having a headband structure, made at least in part of polymeric material, a battery port, a power management network having at least one output lead, and which supplies power from the battery port to the output lead and a multi-pin connector, accessible from outside the headband structure. Also, a video data signal transformation network is supported within the polymeric material of the headband structure and electrically connected to the multi-pin connector. Finally, a first video data signal input to the multi-pin connector is transformed by the video data signal transformation network, which produces a transformed video data signal in response to the first video data signal.

An exemplary method for intelligent compression uses a foveated-compression approach. First, the location of a fixation point within an image frame is determined. Next, the image frame is sectored into two or more sectors such that one of the two or more sectors is designated as a fixation sector and the remaining sectors are designated as foveation sectors. A sector may be defined by one or more tiles within the image frame. The fixation sector includes the particular tile that contains the fixation point and is compressed according to a lossless compression algorithm. The foveation sectors are compressed according to lossy compression algorithms. As the locations of foveation sectors increase in angular distance from the location of the fixation sector, a compression factor may be increased.