Recent development of intelligent object detection systems requires high-definition images for reliable detection accuracy performance, which can cause a high occupation problem of network bandwidth as well as archiving storage capacity. In this paper, we propose an objectness measure-based image compression method of thermal images for machine vision. Based on the objectness of a certain area, bounding box for the area with high objectness is adjusted in order not to affect the possible object detection performance and the image is compressed in a way that the area having a high objectness is compressed with lower compression ratio than other area. The experiments indicate that superior object detection accuracy at comparable BPP is accomplished using the proposed scheme to that of the state-of-the-art video compression method.
The coding objective of image and video that are targeted for machine consumption may differ from that for human consumption. For example, machine may only use a part of image or video requested or required by an application whereas human consumption requires whole captured area of image and video. In addition, machine may require grayscale or certain light spectrum, whereas human consumption requires full visible light spectrum. To identify an object of interest, a neural network based image or video analysis task may be performed and the output of a task is an identified feature (latent) and an associated descriptor (inference). Depending on the usage, multiple tasks can be performed in parallel or in series, and as a number of identified feature increases, the chance of feature area overlap increases as well. We propose a pipeline of descriptor based video coding for machine for multi-task. The proposed method is expected to increase coding efficiency when multiple tasks are performed, by minimizing redundant encoding of overlapped area of objects of interest and to increase utilization and re-utilization of features by transmitting inference separately.
Video stitching technology is widely used to generate Ultra Wide Vision (UWV) and 360VR. However, preserving spatial-temporal coherence and reducing parallax are still challenging problem. In order to increase stitching quality, we propose a parallax-tolerant real-time video stitching method for 12Kⅹ2K UWV. Our experimental results show that the proposed method reduces the parallax efficiently.
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