Let's Explore the Various Types of Video Annotations and Their Applications
                     Let's Explore the Various 
Types of Video Annotations 
Also knowanans vdide oTlabheliengi, rvi deAo pannpotlaiticonais tthei oproncesss  of adding 
meaningful tags, metadata, or labels to objects withina video. This 
process is vital to the success of AI, machine learning (ML), and 
computer vision models that depend on large volumes of annotated 
video data to recognize and interpret the real world. 
 
According to Grand View Research, the global data annotation tools 
market is expected to grow at a CAGR of 26.3% from 2024 to 2030, 
driven largely by the demand for high-quality labeled datasets for 
autonomous systems, retail analytics, and healthcare AI. With the 
explosion of video-based data from surveillance cameras, drones, and 
connected devices, video annotation has become more important 
than ever. 
Let’s explore the major types and real-world applications of 
video annotation. 
Types Of Video 
 Annotations 
Here are the 7 most common types of video annotations used in AI and 
ML development: 
1. Bounding Boxes Bounding boxes remain the most widely used 
and cost-efficient method of video 
annotation. Here, annotators draw rectangular boxes around objects 
appearing in 
video frames to help AI models detect and track them across time. 
Applications 
inc●lu Odbej:e ct detection in autonomous and self-driving 
●vere cR
hi
oe
cl
gtna
e
ii
s
tl 
 
iionnv entory tracking and product 
●●  SVeuhrviceliell adnacmea sgyes taesmsess fsomr eidnetn ftoifry iinnsgu praenocpe Movdeehrnic laensn otation tools now automate parts olfe  tahnisd p rocess using AI-
laasbseislitnegd,  improving both speed and accuracy. 
2. Polygon Annotation When dealing with irregularly shaped 
objects, polygon annotation provides higher 
precision. By marking multiple coordinates around the object, this 
technique allows 
models to accurately understand boundaries and complex contours. 
Common Use 
cas●e sD:r one and satellite imagery for mapping rooftops or terrain 
f●eun 
aStee
u
vl
rfee-
s
nd r
 
roivaindg e cdagre ns avigation, detecting sidewalks, trees, or 
Wit●h  Aagdrviacunlcteusra iln a gneaolystpicast,i aidl eAnI,t ipfyoinlygg ocrno pa nbnooutnadtiaornie hs aasn db ecome 
ienndviirisrrpiogenanmtsioeannb tlpaeal  ftaotnerd rn isn frastructure monitoring. 
3. Semantic Segmentation Semantic segmentation assigns a 
distinct label or color to every pixel in an image, 
classifying all elements in a frame. This method is crucial when AI 
must understand 
contextual relationships between multiple objects. 
Exampl
es:●●   HViertaultahl ctaryre-o inm palatformorgans in scans ging, id
se ndtisiftyininggu itsishs culeost hainndg  types 
● Urban planning, classifying roads, buildings, and 
vegetation 
As of 2025, semantic segmentation powered by deep neural networks 
has become a key component in augmented reality (AR) and industrial 
automation. 
4. Keypoint Annotation This method identifies key points or 
landmarks on an object, such as human joints or 
facial features, to analyze motion, expressions, or interactions. 
Applications 
inc●●lu  FGdaeecs:it al recoroboticsu re-ba
gsneidti ocno natnrodl  esmysotetimons  dfoert egcatmioinn g or 
●●  LTrivaeffiscto flcokw moveanalysis  and 
mpeednets mtroiannit moroinvge ment 
The growing field of behavioral analytics relies heavily on keypoint 
manontoiotna tpiorne dfiocrt ion and ergonomics research. 
5. Landmark Annotation Similar to keypoint annotation, landmark 
(or dot-based) annotation marks multiple 
points on an object’s structure to help AI understand its outline or 
skeleton. 
Use 
cas●e sH:u man pose estimation in sports performance 
a●n Haleyaslitsh patient pcoasrteu raep plications, such as monitoring 
Lan●d mSeanrkti manennot tdaetitoenc thioans  fbreocmo mfaec iianlc mreiacsroinegxlpyr esisgsnioifincsa nt in 
bmioomtioenc-htraancicksin agn tde chnologies used in sports and rehabilitation. 
6. 3D Cuboid Annotation 3D cuboid annotation captures an object’s 
depth, volume, and spatial 
orientation—essential for environments where AI must perceive in 
three dimensions. 
Applications 
inc●lu Aduet:o nomous vehicles, tracking surrounding vehicles and 
p●e Wdeasrterhiaonuss e automation, guiding robotic arms and drones 
● Industrial robotics ensures precision in object 
manipulation 
With the rise of digital twins and 3D perception systems, 3D cuboid 
annotation is now vital in manufacturing and logistics AI systems. 
7. Instance Segmentation Instance segmentation not only identifies 
object categories but also distinguishes 
each unique instance within the same class. It combines object 
detection with 
segmentation for detailed scene understanding. 
Applicatio
ns:●●   SMaetdeilcliatel  iamnadg ainegr,i aslu icmha agse rdye atencatliynsgi sm, duilstitpinleg utuismhionrgs  bour illesions vehicles, and dings, 
ship
p●r oQduuacltitioyn in lisnpeesc tion, identifying multiple defects in s 
This advanced technique forms the backbone of high-precision AI 
dsyiasgtems Howno Estni
ucFs
s
u,
e adu iset
no  Snoomluotuiso nnasv iEgamtipono,w anedr sp rBeduicstiivnee mssaeinste Wnaintche . 
Advanced Video Annotation 
 
Video annotation plays a critical role across industries — from 
automotive and healthcare to retail and manufacturing — but it 
demands precision, scalability, and domain expertise. 
At EnFuse Solutions, we deliver end-to-end data annotation 
isam
eravccug
icee, sa,n iratelyd
n
 i 
cvlun id
deinog la tbeexlti,n g, that empower AI and ML models to perform 
rOeuarl -tweoarmld  leenvveirraognemse hnutsm. an-in-the-loop validation, quality control 
fArIa-amsesiwstoerdk sa, nannd 
Wdahtasets at scal
oet.a tion tools to deliver high-quality, cost-efficient ether you’re building the next autonomous vehicle, enhancing 
med
rAaI,w o
irc al imagin viddeeov edlaotpain
g
 g
  smart surveillance systems, EnFuse helps you turn 
iPnatrot naecrt iwonitahb Elen Finutseel lSigoeluntcieo.n s today to power your AI models with 
eaxnpneorttaltye d video data and drive smarter, faster innovation. 
Read more: 
Self-Driving Cars - Powered With Data Ann
otation