Criminal_Activity_Detector_UCFCrimeDataset

Overview

A video-based binary classifier that labels any clip as Normal or Suspicious. Trained end-to-end on the UCF-Crime dataset — 1,950 annotated surveillance videos across 13 crime categories (Abuse, Arrest, Arson, Assault, Burglary, Explosion, Fighting, Road Accidents, Robbery, Shooting, Shoplifting, Stealing, Vandalism) plus matched normal footage.

Model Usage Code on Google Colab can be found here.

Model Creation Code on Kaggle can be found here.

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Architecture

Raw video
   ↓
ResNet50 (frozen, ImageNet)       ← feature extractor, 25M params
   ↓  16 frames × 2048-dim vectors
LayerNorm + Dense(256) + Dropout(0.4)
   ↓
Bidirectional GRU (64 units)      ← temporal modelling
   ↓
Temporal Attention                ← learned frame weighting
   ↓
Dense(64) + Dropout(0.6)
   ↓
Sigmoid → P(suspicious)

Head parameters: 660,738 (2.52 MB) Backbone: Frozen ResNet50 — features extracted once and cached, not retrained

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Code Snippet 1

import requests

url = "https://shirish9354-ucf-crime.hf.space/predict"

with open(r"FILE_PATH", "rb") as f: res = requests.post(url, files={"file": f})

print(res.json())

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Code Snippet 2

!pip install suspectiq

from suspectiq import SuspectIQ

detector = SuspectIQ()

result = detector.predict(r"FILE_PATH")

print(result)

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Training Details

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Dataset Splits

Splits are at video level — no frame-level leakage between train and val. Val is a stratified 20% carve-out of the training pool.

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Results

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Inference

Supports both recorded video and live streams (webcam, RTSP, RTMP) via a sliding window with configurable overlap:

model = tf.keras.models.load_model('best_model.keras')
# No custom_objects needed — @register_keras_serializable handles it

Latency: ~135 ms per 2-second window on T4 GPU (after tf.function warm-up) Overlap: 50% default — events spanning clip boundaries are always fully captured

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Fixes Applied Across Versions

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