Recently, our world witnessed major events that attracted a lot of attention towards the importance of automatic crowd scene analysis. For example, the COVID-19 breakout and public events require an automatic system to manage, count, secure, and track a crowd that shares the same area. However, analyzing crowd scenes is very challenging due to heavy occlusion, complex behaviors, and posture changes. This paper surveys deep learning-based methods for analyzing crowded scenes. The reviewed methods are categorized as (1) crowd counting and (2) crowd actions recognition. Moreover, crowd scene datasets are surveyed. In additional to the above surveys, this paper proposes an evaluation metric for crowd scene analysis methods. This metric estimates the difference between calculated crowed count and actual count in crowd scene videos.

Detecting moving objects has been well studied in the past due to its importance in computer vision applications. Nevertheless, in aerial imagery, the small sizes of moving objects and the camera motion present challenges to existing well-known detection methods. Most moving object detection methods have reported either high true detection rates associated with high false detection rates, or low false detection rates at the expense of lowering true detection rates. This paper proposes a novel method, KRMARO, to achieve high true detection rates and reduce false detection rates significantly. KRMARO introduces a formulation of the moving objects detection problem which integrates a novel kinematic regularization into the principal component pursuit (PCP). This formulation models moving objects as sparse, that is located in regions exhibiting unique kinematic properties, while the background is modelled as a lowrank matrix that is corrupted by this sparse. To solve the former formulation accurately, KRMARO proposes a solution based on the inexact Newton method and the inexact augmented Lagrange multiplier (IALM) with backtracking behaviour. The robustness of KRMARO is verified through testing on DARPA VIVID, UCF aerial action, and VIRAT aerial datasets and then comparing the results with relevant state-of-the-art methods.

The detection of ground-moving objects in aerial videos has evolved over the years to handle more challenges such as large camera motion, the small size of the objects, and occlusion. Recently, aerial detection has been attempted using principal component pursuit (PCP) due to its superiority in detecting small moving objects. However, PCP-based detection methods generally suffer from high-false detections as well as high-computational loads. This paper presents a novel PCP-based detection method called kinematic regularization with local null space pursuit (KRLNSP) that drastically reduces false detections and the computational loads. KRLNSP models the background in an aerial video as a subspace that spans a low-dimension subspace while it models the moving objects as moving sparse. Accordingly, the detection is achieved by using multiple local null spaces and enhanced kinematic regularization. The multiple local null spaces allow real-time execution to nullify the background while preserving the moving objects unchanged. The kinematic regularization penalizes these moving objects to filter out false detections. The extensive evaluation of KRLNSP and relevant current state-of-the-art methods prove that the KRLNSP outperforms these methods (the true positive rate of KRLNSP is 98% and its false positive rate is 0.4%) and significantly reduces the computational loads (KRLNSP execution time is 0.3 s/frame).