  Video privacy leakage is becoming an increasingly severe public problem,
especially in cloud-based video surveillance systems. It leads to the new need
for secure cloud-based video applications, where the video is encrypted for
privacy protection. Despite some methods that have been proposed for encrypted
video moving object detection and tracking, none has robust performance against
complex and dynamic scenes. In this paper, we propose an efficient and robust
privacy-preserving motion detection and multiple object tracking scheme for
encrypted surveillance video bitstreams. By analyzing the properties of the
video codec and format-compliant encryption schemes, we propose a new
compressed-domain feature to capture motion information in complex surveillance
scenarios. Based on this feature, we design an adaptive clustering algorithm
for moving object segmentation with an accuracy of 4x4 pixels. We then propose
a multiple object tracking scheme that uses Kalman filter estimation and
adaptive measurement refinement. The proposed scheme does not require video
decryption or full decompression and has a very low computation load. The
experimental results demonstrate that our scheme achieves the best detection
and tracking performance compared with existing works in the encrypted and
compressed domain. Our scheme can be effectively used in complex surveillance
scenarios with different challenges, such as camera movement/jitter, dynamic
background, and shadows.
