Metadata-Version: 2.1
Name: dronesearch
Version: 1.0.0.1
Summary: A computer vision pipeline for live video search on drone video feeds leveraging edge servers.
Home-page: https://github.com/cmusatyalab/dronesearch
Author: Junjue Wang
Author-email: junjuew@cs.cmu.edu
License: Apache License 2.0
Description: # Overview
        
        This repo contains a python package [dronesearch](dronesearch) for running live
        video analytics on drone video feeds leveraging edge servers. It also contains
        our experiment code for SEC'18 paper *[Bandwidth-efficient Live Video Analytics
        for Drones via Edge Computing](https://ieeexplore.ieee.org/document/8567664)*.
        
        ## dronesearch Package
        
        The decreasing costs of drones have made them suitable for search and rescue
        tasks. Analyzing drone video feeds in real-time can greatly improve the
        efficiency of search tasks. However, typical drone platforms do not have enough
        computation power to do real-time video analysis onboard, especially
        semantic-level vision processing, such as human survivor detection, car
        detection, and animal detection. Video feeds need to be streamed to an edge
        server for computer vision processing. When streaming video feeds from a swarm
        of drones at the same time, judicious use of bandwidth becomes important.
        
        This [dronesearch](dronesearch) package provides a computer vision pipeline that
        selectively finds interesting frames and transmit them to edge servers for
        analysis in order to save bandwidth.
        
        ### Installation
        
        First, install [zeromq](https://zeromq.org/download/). Then,
        
        ```bash
        pip install dronesearch
        ```
        
        ### Demo
        
        We provide a demo that considers *computer monitors* as objects of interests.
        Only video frames that are classified as *computer monitors* will be sent to an
        edge server for further analysis.
        
        To run the demo, first clone this directory. Then, issue the following commands
        at the root dir of this repo.
        
        ```bash
        # on drone or your drone emulation platform, by default connecting to tcp://localhost:9000
        # --input-source: the uri for OpenCV's VideoCapture(). 
        #                 It should be a number for cameras or a file path for videos.
        # --filter-config-file: a file path whose content specifies filters to run on the drone.
        #                       This demo uses Tensorflow's MobileNet.
        # --server-host, and --server-port specifies the edge server.
        python -m dronesearch.onboard --input-source 0 --filter-config-file data/cfg/filter_config.ini
        
        # on edge server
        # --server-port specifies the listening port.
        python -m dronesearch.onserver
        ```
        
        ## Experiments for SEC'18 paper
        
        See [experiment-README](experiment-README.md).
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Requires-Python: >3.5, <4
Description-Content-Type: text/markdown
