shirish9354/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

Setting	Value
Framework	TensorFlow 2.19.0 / Keras 3
GPU	2× Tesla T4 (Kaggle)
Loss	Focal Loss (α=0.60, γ=2.0)
Optimiser	Adam + gradient clipping (clipnorm=1.0)
LR schedule	ReduceLROnPlateau on val_AUC, min 1e-5
Early stopping	Patience 6 on val_AUC, restore best weights
Epochs run	19 (best at epoch 13)
Batch size	32
Sequence length	16 frames per 2-second clip
Threshold	0.325 (macro-F1 tuned on validation set)

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Dataset Splits

Split	Clips	Normal	Suspicious
Train	1,328	680 (51.2%)	648 (48.8%)
Val	332	170 (51.2%)	162 (48.8%)
Test	290	150 (51.7%)	140 (48.3%)

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

Metric	Value
Test accuracy	90.3%
Test AUC-ROC	0.930
Macro F1	0.903
Normal precision / recall	89.6% / 92.0%
Suspicious precision / recall	91.2% / 88.6%
False alarms (FP)	12 / 150 normal clips
Misses (FN)	16 / 140 suspicious clips

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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

Issue	Fix
Class weight collapse (v1)	Focal Loss α=0.60
Backbone overfitting	ResNet50 frozen throughout
Slow extraction	Batched GPU extraction (~8× faster)
Threshold bias	Macro-F1 tuning instead of suspicious-only F1
Custom class serialisation	@register_keras_serializable
Training resume after crash	Epoch history + weight checkpoint
Feature extraction resume	Incremental partial .npz checkpoint every 200 clips
Dataset path resolution	BFS auto-discovery + 5-layout fallback
Leakage	Video-level splits confirmed

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