Film Profile · FAST-AND-FURIOUS
Fast And Furious
Detections71,923
Night Frames69%
Dialogue WPM33
Dom. Emotionneutral
GENOME
Movie DNA
Six core dimensions, each scored against the genre baseline.
Darkness-29% vs genre
34.7/100
Bright
Action Intensity-17% vs genre
52/100
Moderate
People Density-40% vs genre
44/100
Intimate
Indoor / Outdoor
20% indoor
Outdoor · 19.6% indoors
Object Diversity
105/100
High
Pacing
0.9 cuts/min
Dynamic · cuts/min
FINGERPRINT
The full shape, vs. genre
Six axes overlaid against the average for this film's primary genre.
Fast And FuriousComedy avg · 16 titles
THE ARC
Motion & brightness across the runtime
Hover to scrub. Spikes mark high-action sequences. Dips reveal quiet stretches.
Motion Intensity
Brightness
People
STRUCTURE
Scene mix by act
Three-act split — how the visual tone shifts from setup through climax.
PEAKS & VALLEYS
When the film pushes — and breathes
The single most-intense minute and longest stretch of stillness.
CHROMA
Color signature
Frame-sampled palette — temperature, saturation, and how the look shifts across the runtime.
Temperaturewarm
Saturationvivid
Frames sampled5,120
Film color bandhow the palette shifts across the runtime · 60 buckets
0:00¼½¾end
Overall palette6 dominant colors · % of sampled frames
#4C311A
23.6%
#998567
20.6%
#576360
16.4%
#75532C
16.0%
#17424F
12.7%
#C0BCAB
10.7%
Palette by scene type5 dominant colors per context · sampled within scene class
Empty scenes
#A08A6B
#836745
#553B1D
#505149
#C1B8A0
Intimate scenes
#0F404C
#533118
#4C5655
#8C6E46
#BAB08E
Night scenes
#54361B
#666457
#184451
#978E75
#CDCDBE
Outdoor scenes
#55381B
#686254
#9C8969
#1C4651
#C3BEAD
Romantic scenes
#715F45
#A17F59
#4A3625
#A29682
#C2B8A8
Vibrant scenes
#745228
#946B34
#206675
#74340B
#C19137
DETECTIONS
What appears on screen most
The four most-present objects — full breakdown with categories in Deep Dive.
ON SCREEN
Cast at a glance
Top 3 actors by screen time, color-coded by dominant emotion.
VD
Vin Diesel
Dominic Toretto
21.7m · angry
MR
Michelle Rodriguez
Letty Ortiz
1.8m · sad
SK
Sung Kang
Han Lue
0.9m · sad
AI-DISCOVERED FACTS
AI-Discovered Facts — Fast And Furious (2009)
Insights surfaced by machine analysis of every frame, audio track, and object.
- Fast and Furious has over 22 vehicles per film, outpacing the average of 22 vehicles per 100 films in the Comedy genre.
- 3,823 unique people spotted (73% of frames)
- "bragg" is said 17 times — once every 6.3 minutes.
- Vin Diesel is on screen for 22 of 107 minutes — 20% of the runtime.
- The longest stretch of silence runs 8 min 38 sec — no dialogue at all.
- Only 4.3% of all detected faces are smiling across the entire film.
- At 134 BPM, the soundtrack rarely lets up — one of the faster-paced scores in the database.
- 1,136 unique words spoken out of 3,565 total — a vocabulary richness of 31.9%.
- AI detected 105 unique object types across 71,923 frame-by-frame detections.
- 1,508 unique cars spotted (17% of frames)
- 346 unique ties spotted (7% of frames)
- 347 unique chairs spotted (5% of frames)
- Police cars appear in 5% of frames
- 190 unique bottles spotted (2% of frames)
- 248 unique trucks spotted (3% of frames)
- 166 unique tvs spotted (2% of frames)
- 94 unique cups spotted (2% of frames)
- 98 unique cell phones spotted (2% of frames)
- 9,806 cars spotted (vehicle-heavy film)
- 130 weapon appearances (1 per minute)
- Contains 13 profanities (0.1 per minute)
- Longest silence: 9 minutes of unbroken quiet
- 31% of frames are close-ups
- Only 4.3% of face detections are smiling
- The film's emotional journey ends on a neutral note
- Shot predominantly with warm tones
- High color saturation — vivid visual style
DIALOGUE
Words across the runtime
Volume and pace at a glance. Top vocabulary and per-act WPM in Deep Dive.
48% dialogue52% silence / score
3,565
total words
33
words / min
13 · 0.4%
profanity flagged
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