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    "info": {
        "author": "Junjue Wang",
        "author_email": "junjuew@cs.cmu.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Developers",
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            "Programming Language :: Python :: 3.6",
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            "Topic :: Scientific/Engineering"
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        "description": "# Overview\n\nThis repo contains a python package [dronesearch](dronesearch) for running live\nvideo analytics on drone video feeds leveraging edge servers. It also contains\nour experiment code for SEC'18 paper *[Bandwidth-efficient Live Video Analytics\nfor Drones via Edge Computing](https://ieeexplore.ieee.org/document/8567664)*.\n\n## dronesearch Package\n\nThe decreasing costs of drones have made them suitable for search and rescue\ntasks. Analyzing drone video feeds in real-time can greatly improve the\nefficiency of search tasks. However, typical drone platforms do not have enough\ncomputation power to do real-time video analysis onboard, especially\nsemantic-level vision processing, such as human survivor detection, car\ndetection, and animal detection. Video feeds need to be streamed to an edge\nserver for computer vision processing. When streaming video feeds from a swarm\nof drones at the same time, judicious use of bandwidth becomes important.\n\nThis [dronesearch](dronesearch) package provides a computer vision pipeline that\nselectively finds interesting frames and transmit them to edge servers for\nanalysis in order to save bandwidth.\n\n### Installation\n\nFirst, install [zeromq](https://zeromq.org/download/). Then,\n\n```bash\npip install dronesearch\n```\n\n### Demo\n\nWe provide a demo that considers *computer monitors* as objects of interests.\nOnly video frames that are classified as *computer monitors* will be sent to an\nedge server for further analysis.\n\nTo run the demo, first clone this directory. Then, issue the following commands\nat the root dir of this repo.\n\n```bash\n# on drone or your drone emulation platform, by default connecting to tcp://localhost:9000\n# --input-source: the uri for OpenCV's VideoCapture(). \n#                 It should be a number for cameras or a file path for videos.\n# --filter-config-file: a file path whose content specifies filters to run on the drone.\n#                       This demo uses Tensorflow's MobileNet.\n# --server-host, and --server-port specifies the edge server.\npython -m dronesearch.onboard --input-source 0 --filter-config-file data/cfg/filter_config.ini\n\n# on edge server\n# --server-port specifies the listening port.\npython -m dronesearch.onserver\n```\n\n## Experiments for SEC'18 paper\n\nSee [experiment-README](experiment-README.md).\n\n\n",
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        "license": "Apache License 2.0",
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        "name": "dronesearch",
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