An anomaly in surveillance is defined as any suspicious activity in video footage. An object with an unusual motion pattern or trajectory entering the frame is considered an anomaly. Examples include bike accidents or a person driving at high speed in a crowded place. This paper focuses on detecting anomalies in single-scene surveillance footage, as it is particularly useful for real-world applications where cameras are located at a fixed position. An unsupervised approach is used, employing an autoencoder and decoder model to minimize the reconstruction error between the input and generated output. The model is trained, and the reconstruction error and training loss are observed. After training, regularity scores are calculated for each test case. The proposed model outperforms other models in many scenarios, achieving a precision of 0.87 at a standard threshold of 0.75, which is better compared to other encoder-decoder models. However, the accuracy of the model decreases as the threshold increases.