suspectiq 1.0.1

Video anomaly / suspicious-activity detection using ResNet50 + Temporal Attention.

SuspectIQ

Video anomaly / suspicious-activity detection in one line.

Installation

pip install .          # from the cloned repo
# or, once published:
pip install suspectiq

Requirements: Python ≥ 3.9, TensorFlow ≥ 2.12, OpenCV, NumPy.

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Quick start

from suspectiq import SuspectIQ

# Load once – models are lazy-loaded on the first predict() call
detector = SuspectIQ("best_model_ucf.keras")

result = detector.predict("clip.mp4")

print(result.label)       # "NORMAL" or "SUSPICIOUS"
print(result.confidence)  # confidence for the predicted class
print(result.probability) # raw model probability (SUSPICIOUS)

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API reference

SuspectIQ(model_path, threshold=0.325, num_frames=16, img_size=224)

Parameter	Type	Default	Description
model_path	str	—	Path to the .keras temporal model file.
threshold	float	0.325	Decision boundary for SUSPICIOUS classification.
num_frames	int	16	Frames sampled per clip.
img_size	int	224	Frame resize dimension (height = width).

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detector.predict(video_path) → PredictionResult

Classify a single video file.

result = detector.predict("footage.mp4")

Returns a PredictionResult with:

• result.label – "NORMAL" or "SUSPICIOUS"
• result.confidence – confidence for the winning class (0 – 1)
• result.probability – raw SUSPICIOUS probability from the model

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detector.predict_batch(video_paths) → list[PredictionResult | None]

Classify multiple videos. Failed videos return None.

results = detector.predict_batch(["a.mp4", "b.mp4", "c.mp4"])
for path, result in zip(paths, results):
    if result:
        print(f"{path}: {result.label} ({result.confidence:.2%})")

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detector.set_threshold(threshold)

Adjust sensitivity at runtime without reloading models.

detector.set_threshold(0.5)   # stricter – fewer SUSPICIOUS flags
detector.set_threshold(0.2)   # more sensitive – more SUSPICIOUS flags

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PredictionResult

@dataclass
class PredictionResult:
    label: str        # "NORMAL" | "SUSPICIOUS"
    confidence: float # confidence for the predicted class
    probability: float # raw model output (P(SUSPICIOUS))

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Model architecture

Stage	Component
Frame sampling	OpenCV – first num_frames frames
Feature extractor	ResNet50 (ImageNet, frozen)
Temporal model	Custom model with TemporalAttention
Output	Sigmoid → threshold → label