The performance of the auroral large imagining system (ALIS_4D) and an automatic track detection algorithm was evaluated for space debris surveillance and tracking. The evaluation of the ALIS_4D was done through a numerical simulation and data annotations, while the track detection results were manually evaluated. The effect of auroral conditions, filters, and the detection mode were evaluated for the performance of both.  It was found that  ALIS_4D can detect resident space objects. The peak detection rate per hour was dependent on the time of the year, day, and the limiting magnitude set by the filters and the sensor among others. The peak was simulated to be approximately 120 in January and 70 in April and September. A space object observation campaign was performed in April 2020 for 90 minutes. During that period across the used four stations 61 unique objects were detected and 37 unique objects were detected at the Abisko station, that was used for the simulation. During the observation time there was auroral activity which can block the line-of-sight to resident space objects.  The track detection algorithm was evaluated for data gathered in a dedicated space situational awareness (SSA) mode and other modes. In SSA mode, the algorithm found 60% of the subsections of the image with visible traces. The false detection rate was 17% when no auroras were present and 56% when there were. In other modes the evaluation was simplified due to large number of false positives. When assumed best case scenario 99.2% of the detections were false. The auroral activity and the used mode had the most significant effect on the track detection algorithm performance. It was found that in SSA mode the used filter did not effect on the track detection performance.