[HN Gopher] Launch HN: Sieve (YC W22) - Pluggable APIs for Video...
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Launch HN: Sieve (YC W22) - Pluggable APIs for Video Search
Hi HN, we're Mokshith and Abhi from Sieve (https://sievedata.com).
We're building an API that lets you add video search to internal
tools or customer applications, instantly. Sieve can process 24
hours of video in less than 10 minutes, and makes it easy to search
video by detected objects / characteristics, motion data, and
visual similarity. You can use our models out of the box, or plug-
in your own model endpoints into our infrastructure. ('Model' here
means any software that produces output given an image.) Every
industry from security, to media, supply chain, construction,
retail, sports, and agriculture is being transformed by video
analytics--but setting up the infrastructure to process video data
quickly is difficult. Having to deal with video ingestion
pipelines, computer-vision model training, and search functionality
is not pretty. We're building a platform that takes care of all of
this so teams can focus on their domain-expertise, building
industry-specific software. We met in high school, and were on the
robotics team together. It was our first exposure to computer
vision, and something we both deeply enjoyed. We ended up going to
UC Berkeley together and worked on computer vision at places like
Scale AI, Niantic, Ford, NVIDIA, Microsoft, and Second Spectrum. We
were initially trying to solve problems for ourselves as computer
vision developers but quickly realized the unique problems in video
having to do with cost, efficiency, and scale. We also realized how
important video would be in lots of verticals, and saw an
opportunity to build infrastructure which wouldn't have to be
rebuilt by a fullstack dev at any company again. Let's take the
example of cloud software for construction which might include tons
of features from asset trackers to rental management and compliance
checks. It doesn't make sense for a construction software company
to build their own video processing for telematics--the density and
scale of video make this a difficult task. A single 30 FPS camera
generates over 2.5M frames within a day of recording. Imagine this
across thousands of cameras and many weeks of footage--not to
mention the actual vertical-specific software they're building for
end users. Sieve takes care of everything hard about processing
and searching video. Our API allows you to process and search video
with just two API calls. We use filtering, parallelization, and
interpolation techniques to keep costs low, while being able to
process 24 hours of video in under 10 minutes. Users can choose
from our pre-existing set of models, or use their own models with
our video processing engine. Our pricing can range anywhere from
$0.08-$0.45 per minute of video processed based on the models
clients are interested in and usage volume. Our FAQ page
(https://sievedata.com/faq) explains these factors in more detail.
Our backend is built on serverless functions. We split each video
into individual chunks which are processed in parallel and passed
through multiple layers of filters to determine which chunks are
"important". We're able to algorithmically ignore parts of video
which are static, or change minimally, and focus on the parts that
contain real action. We then run more expensive models on the most
"important" parts of video, and interpolate results across frames
to return information to customers at 30 FPS granularity. Our
customers simply push signed video URLs to our platform, and this
happens automatically. You can then use our API to query for
intervals of interest. We haven't built an automated sign up flow
yet because we're focused on building out the core product for now.
But we wanted to give all of you the chance to try Sieve on your
own videos for free, so we've set up a special process for HN
users. Try it out here: https://sieve-data.notion.site/Trying-
Sieve-s-Video-Search-4.... We'll email you a personal, limited-
access API key. Here's a video demo of using our dashboard to do
video search: https://www.youtube.com/watch?v=_uyjp_HGZl4 We'd
love to hear what you think about the product and vision, and ideas
on how we can improve it. Thanks for taking the time to read this,
we're grateful to be posting here :)
Author : mvoodarla
Score : 44 points
Date : 2022-02-02 18:25 UTC (4 hours ago)
(HTM) web link (sievedata.com)
(TXT) w3m dump (sievedata.com)
| plasma wrote:
| Neat project, you may want to consider adding audio processing
| too (eg sound detected) as part of the video.
|
| You could go deeper and compare samples of audio that could be
| uploaded separately (eg, siren sounds), check out MFCC processing
| https://en.wikipedia.org/wiki/Mel-frequency_cepstrum#Applica...
| to do Shazam-style audio comparison.
|
| I wonder too if you process it on a per-frame basis or you can
| take series of frames too (eg analyze the last 5 seconds of
| frames) to detect things like a "hand wave".
| mvoodarla wrote:
| Thanks for the comment! Audio is definitely an interesting
| angle. We haven't spoken to a lot of folks yet that want that
| feature, but I'd guess some productivity tools that let you
| rewatch Zoom calls, baby monitoring, media playback, or a few
| other things like that might mind audio useful. We'll explore
| it for sure.
|
| We currently process on the per-frame basis but have the
| optionality to increase the window size of 1, to any arbitrary
| size like 300 frames. This is what allows us to currently
| detect "actions" for some of our customers. Of course, it's not
| the same as a sliding window but it's something we're
| considering.
| applgo443 wrote:
| This is really cool!
|
| I have a few questions
|
| 1. Is there really no other competitor or company that tried to
| tackle this problem in the past? It feels like a really common
| usecase and someone must've done something about it!
|
| 2. Do you have a fixed set of words that the user should use to
| query? I'm an AI researcher/practitioner who worked in this area.
| It's super difficult to search for tail objects in images/text.
|
| 3. Why API first?
| mvoodarla wrote:
| Thanks for the thoughtful questions!
|
| 1. People have definitely tried this in the past in terms of
| having similar tech, but I don't think anyone has sanely
| approached it at the same angle. We're seeing so many more
| companies like EquipmentShare
| (https://www.equipmentshare.com/), UpKeep
| (https://www.upkeep.com/), Spot AI (https://www.spot.ai/) and
| others which are building core software for industries without
| necessarily having tons of video expertise in-house. Also other
| video-first companies like Gong (https://www.gong.io/), Mux
| (https://mux.com/), and Loom (https://www.loom.com/) are
| building platforms that might benefit from visual analysis at
| scale. Things like serverless functions were also less cool
| before.
|
| 2. There's a bunch of ways to search using Sieve. Some are
| fixed variables, others are visual similarity based. See here:
| https://sievedata.com/faq
|
| 3. Our target is the full stack developer within companies
| building cloud-first software for specific industries. Easy-to-
| use APIs are key because of this. They are the ones with the
| expertise to package it into a product that really works for
| the end user, and we're the ones with computer-vision and video
| expertise.
| jensneuse wrote:
| Hey, that's a really nice product. I love that it's API first?
| Would you be interested in adding it to our API hub? We're
| currently in private beta and would love to have your API on it.
| Why? It's the best developer experience to integrate APIs and
| we'd love to send our users your way. Here's a link if you're
| interested: https://hub.wundergraph.com/
| mvoodarla wrote:
| Thanks! The API hub seems interesting. Want to send me an email
| (see bio)?
| jensneuse wrote:
| Sure!
| PanMan wrote:
| Like the concept, but especially for live feeds, seems expensive?
| at $3456/month/camera ? Is this not your use-case (it's the first
| one on your use-cases page)? Or am I missing something? Congrats
| on the launch!
| mvoodarla wrote:
| Thanks!
|
| Most use-cases have limitations in bandwidth, so not all data
| is streamed up to the cloud. Typically, people will run simple
| motion detectors on the physical camera device itself, and save
| content in the cloud that they think might be valuable. Our
| service can then be used to make that content searchable, past
| simple motion detection.
|
| There are other use-cases where people store tons of data in
| the cloud already. Media content is an example of this. In this
| case, companies might want to analyze all the raw data for
| which we then charge only for intervals of video where there's
| real action. If the video is static, we won't charge the full
| per minute pricing.
| idomi wrote:
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